JP3995854B2 - Image processing method and apparatus, and recording medium - Google Patents

Description

これは、ガウス信号は周波数空間および実空間の双方において局在性がよいためであり、例えば上記（１）式においてσ＝１とした場合の５×１の１次元フィルタは図５に示すようなものとなる。
【００６３】
フィルタリング処理は、図６に示すように、原画像信号Ｓorgに対して、あるいは低解像度画像信号に対して１画素おきに行なう。このような１画素おきのフィルタリング処理をｘ方向、ｙ方向に行なうことにより、低解像度画像信号Ｌ_１の画素数は原画像の１／４となり、フィルタリング処理により得られる低解像度画像信号に対して繰り返しこのフィルタリング処理を施すことにより、得られるｎ個の低解像度画像信号Ｌ_ｋ（ｋ＝１〜ｎ）は、それぞれ画素数が原画像信号Ｓorgの１／２^２ｋの画像信号となる。
【００６４】
次に、このようにして得られた低解像度画像信号Ｌ_ｋに対して施される補間処理について説明する。補間処理を行なうための補間演算の方法としては、Ｂスプラインによる方法等種々の方法が挙げられるが、本実施形態においては、上記フィルタリング処理においてガウス信号に基づくローパスフィルタを用いているため、補間演算についてもガウス信号を用いるものとする。具体的には、下記の式（２）において、σ＝２^ｋ−１ と近似したものを用いる。
【００６５】
【数２】

例えば低解像度画像信号Ｌ_１を補間する際には、ｋ＝１であるためσ＝１となる。この場合、補間処理を行なうためのフィルタは、図７に示すように５×１の１次元フィルタとなる。この補間処理は、まず低解像度画像信号Ｌ_１に対して１画素おきに値が０の画素を１つずつ補間することにより低解像度画像信号Ｌ_１を原画像と同一の画素数となるように拡大し、次に、この補間された低解像度画像信号Ｌ_１に対して上述した図７に示す１次元フィルタによりフィルタリング処理を施すことにより行なわれる。
【００６６】
同様に、この補間拡大処理を全ての低解像度画像信号Ｌ_ｋに対して行なう。低解像度画像信号Ｌ_ｋを補間する際には、上記式（２）に基づいて、３×２^ｋ−１の長さのフィルタを作成し、低解像度画像信号Ｌ_ｋの各画素の間に値が０の画素を１個ずつ補間することにより、１段階高解像度の低解像度画像信号Ｌ_ｋ−１と同一画素数となるように拡大し、この値が０の画素が補間された低解像度画像信号Ｌ_ｋに対して３×２^ｋ−１の長さのフィルタにより、フィルタリング処理を施すことにより補間拡大してボケ画像信号Ｓuskを得る。
【００６７】
次に、上記のようにして作成されたボケ画像信号Ｓuskが、対応する画素数を有する低解像度画像信号Ｌ_ｋ−１から減算されて、帯域制限画像信号Ｂ_ｋ（ｋ＝１〜ｎ）が得られる。なお、帯域制限画像信号Ｂ_ｋは下記の式（３）に示すものとなる。
【００６８】
Ｂ_１＝Ｓorg−Ｓus１
Ｂ_２＝Ｌ_１−Ｓus２
Ｂ_３＝Ｌ_２−Ｓus３
・ （３）
・
Ｂ_ｋ＝Ｌ_ｋ−Ｓusｋ
具体的には、図４に示すように５段階の低解像度画像信号Ｌ_１〜Ｌ_５が得られた場合、まず最低解像度の低解像度画像信号Ｌ_５に対して補間処理を施して、低解像度画像信号Ｌ_４と同一画素数を有するボケ画像信号Ｓus５を作成する。そして、低解像度画像信号Ｌ_４からボケ画像信号Ｓus５を減算して帯域制限画像信号Ｂ_５を得る。以下順次Ｌ_３−Ｓus４、Ｌ_２−Ｓus３、Ｌ_１−Ｓus２、Ｓorg−Ｓus１の演算を行なって、帯域制限画像信号Ｂ_１〜Ｂ_５を得る。ここで、最低解像度の低解像度画像信号Ｌ_ｋ（Ｌ_５）は、原画像を縮小した低周波の情報を表すものであるが、これ以降の演算において使用することはない。
【００６９】
次に、上述したように算出された帯域制限画像信号Ｂ_ｋを用いて行なわれる処理について説明する。図８は処理手段３の構成を帯域制限画像信号作成手段２とともに示す概略ブロック図である。図８に示すように、帯域制限画像信号作成手段２において作成された帯域制限画像信号Ｂ_ｋは、本発明のノイズ信号生成手段としてのノイズ分離手段２２においてノイズ成分が分離されて、ノイズ帯域制限画像信号ＮＢ_ｋが得られる。ここで、ノイズ分離手段２２におけるノイズ成分の分離処理について説明する。
【００７０】
図９はノイズ分離手段２２の構成を示す概略ブロック図である。ノイズ分離手段２２は、アイリスフィルタを用いた処理により、帯域制限画像信号Ｂ_ｋからノイズ成分を分離するものであり、入力された帯域制限画像信号Ｂ_ｋの全画素について、この帯域制限画像信号Ｂ_ｋに基づく各画素毎の信号勾配ベクトルを算出する勾配ベクトル算出手段３１と、全画素のうち１つを順次、注目画素として設定する注目画素設定手段３４と、注目画素設定手段３４により設定された注目画素を中心として、所定の角度間隔（例えば11.25 度間隔）で隣接する複数（例えば32本）の放射状の方向線（図１０参照）を設定する方向線設定手段３３と、設定された各方向線毎に、注目画素から予め定められた所定範囲内にある方向線上の各画素について、各画素の勾配ベクトルとこの方向線の延びる方向とのなす角度θil（32本の方向線のうち第ｉ番目の方向線上における、注目画素から第ｌ番目の画素の勾配ベクトルと、この第ｉ番目の方向線の延びる方向とのなす角度を表す）に基づく指標値cosθilをそれぞれ求める指標値算出手段３５と、注目画素を始点とし、終点を前記予め設定された範囲に応じた大きさまで変化させて、始点から終点の範囲内にある方向線上の各画素の指標値cosθilの平均値Ｃｉ（ｎ）を、各方向線毎に下記の式（４）にしたがって求めるとともに、この平均値のうち最大値Ｃimax（式（５））を抽出する最大値算出手段３６と、各方向線毎に抽出された最大値Ｃimaxを32本の方向線の全てについて加算平均（（ΣＣimax）／32）してこの注目画素についての勾配ベクトル群の集中度の値Ｃ（式（６））を算出する集中度算出手段３７と、
【数３】

【数４】

集中度の値Ｃが大きいほど注目画素が画像中におけるエッジ上に位置するものとして大きな重み付けとなり、集中度の値Ｃが小さいほど注目画素がエッジ以外に部分に存在するものとして小さな重み付けとなるように、空間フィルタのフィルタ係数を設定するフィルタ設定手段３８と、フィルタ設定手段３８においてフィルタ係数が設定された空間フィルタにより、帯域制限画像信号Ｂ_ｋに対してフィルタリング処理を施してフィルタ処理済み帯域制限画像信号ＦＢ_ｋを得るフィルタリング手段３９と、帯域制限画像信号Ｂ_ｋからフィルタ処理済み帯域制限画像信号ＦＢ_ｋを減算してノイズ帯域制限画像信号ＮＢ_ｋを算出する信号算出手段４０とを備える。
【００７１】
なお、アイリスフィルタについては、「小畑他、ＤＲ画像における腫瘤影検出（アイリスフィルタ）、電子情報通信学会論文誌 D-II Vol.J75-D-II No.3 P663〜670 1992年３月参照」、「小畑他、アイリスフィルタとその特性解析、計測自動制御学会論文集、1998 VOL.34 No.4、p.326-332」にその詳細が記載されている。このアイリスフィルタ処理は、特に乳癌における特徴的形態の１つである腫瘤陰影を検出するのに有効な手法として研究されており、アイリスフィルタ処理に用いられるアイリスフィルタは、画像信号の勾配を勾配ベクトルとして算出し、その勾配ベクトルの集中度を出力するものであり、アイリスフィルタ処理とはこの勾配ベクトルの集中度を基に腫瘤陰影を検出するものである。本実施形態においては、このアイリスフィルタ処理により、帯域制限画像信号Ｂ_ｋの勾配ベクトルの集中度から各画素がエッジなどの線分上に位置する程度を求めるものである。
【００７２】
勾配ベクトル算出手段３１は、詳しくは例えば図１１に示した縦５画素×横５画素の大きさのマスクの最外周部の画素の画像データ（画素値）を用いて、下記の式（７）にしたがって信号勾配ベクトルの向きθを求める。
【００７３】
【数５】

このマスクサイズは縦５画素×横５画素のもの限るものではなく、種々の大きさのものを用いることができる。
【００７４】
方向線設定手段３３が設定する方向線の数は、上記32本に限るものではないが、余りに多く設定すると計算処理に要する負担が急激に増大し、また少な過ぎればエッジ成分を精度よく検出することができないので、32本程度が好ましい。またこの方向線間の角度間隔は等間隔とするのが計算処理等において都合がよい。
【００７５】
集中度算出手段３７において算出される集中度の値Ｃが大きな値となるのは、勾配ベクトルの向きが注目画素に集中する場合である。
【００７６】
フィルタ設定手段３８においては集中度算出手段３７において求められた集中度の値Ｃに応じて平滑化処理を行なうための空間フィルタのフィルタ係数が設定される。すなわち、集中度の値Ｃが大きいほど注目画素はエッジ上に存在し、集中度の値Ｃが小さいほど注目画素はエッジ以外の位置に存在することとなる。したがって、集中度の値Ｃが大きいほど重み付けが大きくなるようなフィルタ係数が定められて、空間フィルタが設定されることとなる。
【００７７】
具体的には、まず集中度の値Ｃが所定の閾値よりも高い画素の値を１、所定の閾値以下の画素の値を０とする２値化処理を行ない、エッジ成分とそれ以外の成分との分離を行なう。ここで、基本となる空間フィルタをＦ０とし、この空間フィルタＦ０が３×３の平滑化フィルタであり、そのフィルタ係数が図１２（ａ）に示すものであるとすると、集中度Ｃの２値化結果に応じてフィルタＦ０のフィルタ係数が重み付けされて帯域制限画像信号Ｂ_ｋに対して施す空間フィルタＦ１のフィルタ係数が設定される。すなわち、ある注目画素がエッジ成分上にあり、この注目画素を中心とする３×３の範囲の集中度Ｃ（２値化後）が図１２（ｂ）に示すものであった場合、空間フィルタＦ１のフィルタ係数は図１２（ｃ）に示すものとなる。また、ある注目画素がエッジ成分以外の部分にあり、注目画素を中心とする３×３の範囲の集中度Ｃが図１２（ｄ）に示すものであった場合、空間フィルタＦ１のフィルタ係数は図１２（ｅ）に示すものとなる。したがって、この空間フィルタＦ１により帯域制限画像信号Ｂ_ｋを平滑化すると、エッジ成分はぼかされることなく、それが存在する方向に対してぼかされることとなる。また、エッジ成分以外の成分は値が０とされることとなる。
【００７８】
フィルタリング手段３９は、フィルタ設定手段３８において設定された空間フィルタＦ１により、帯域制限画像信号Ｂ_ｋに対してフィルタリング処理を施してフィルタ処理済み帯域制限画像信号ＦＢ_ｋを得る。このフィルタリング処理により、帯域制限画像信号Ｂ_ｋは平滑化されるが、エッジ成分においては、エッジが存在する方向に平滑化されることとなる。これにより、フィルタ処理済み帯域制限画像信号ＦＢ_ｋにおいては、平滑化されたエッジ成分のみが残ることとなる。
【００７９】
信号算出手段４０においては、帯域制限画像信号Ｂ_ｋからフィルタ処理済み帯域制限画像信号ＦＢ_ｋを減算してノイズ帯域制限画像信号ＮＢ_ｋが得られる。ここで、フィルタ処理済み帯域制限画像信号ＦＢ_ｋは平滑化されているため、ノイズ帯域制限画像信号ＮＢ_ｋは、帯域制限画像信号Ｂ_ｋに含まれるノイズ成分を表すものとなる。とくに、フィルタ処理済み帯域制限画像信号ＦＢ_ｋはエッジ成分が存在する方向に平滑化されているため、求められたノイズ成分にはエッジ上におけるノイズも含まれることとなる。
【００８０】
ノイズ分離手段２２に入力された帯域制限画像信号Ｂ_ｋは、まず勾配ベクトル算出手段３１、注目画素設定手段３４、フィルタリング手段３９および信号算出手段４０に入力される。勾配ベクトル算出手段３１は前述したように縦５画素×横５画素の大きさのマスクの最外周部の画素の画像データ（画素値）を用いて、全画素について信号勾配ベクトルの向きθを求める。求められた信号勾配ベクトルの向きθは指標値算出手段３５に入力される。
【００８１】
一方、注目画素設定手段３４は、入力された帯域制限画像信号Ｂ_ｋについて、その全画素のうち１つを順次注目画素として設定し、設定した注目画素を方向線設定手段３３に入力する。方向線設定手段３３は、注目画素を中心として例えば11.25 度の等間隔で隣接する32本の放射状の方向線を設定する。そしてこの設定された方向線は指標値算出手段３５に入力される。
【００８２】
指標値算出手段３５は、勾配ベクトル算出手段３１から入力された勾配ベクトルの向きθが定義された、帯域制限画像信号Ｂ_ｋと同じ２次元に配列された画素に、方向線設定手段３３から入力された32本の方向線を重ねて、この32本の方向線にそれぞれ重なる画素を抽出する。
【００８３】
さらに指標値算出手段３５は、各方向線毎に各画素に定義された勾配ベクトルの向きθとこの方向線の延びる方向とのなす角度θil（32本の方向線のうち第ｉ番目の方向線上における、注目画素から第ｌ（エル）番目の画素の勾配ベクトルと、この第ｉ番目の方向線の延びる方向とのなす角度を表す）に基づく指標値cosθilをそれぞれ求める。
【００８４】
この求められた各方向線上における各画素の指標値cosθilは最大値算出手段３６に入力される。最大値算出手段３６は、上記始点から終点の範囲内にある方向線上の各画素の指標値cosθilの平均値Ｃｉ（ｎ）を、各方向線毎に求めるとともに、この平均値Ｃｉ（ｎ）の最大値Ｃimaxを抽出する。
【００８５】
このように各方向線毎に求められた最大値Ｃimaxは集中度算出手段３７に入力される。集中度算出手段３７は入力された各方向線毎に求められた最大値Ｃimaxを加算平均して、その注目画素についての勾配ベクトル群の集中度の値Ｃを算出する。算出された勾配ベクトル群の集中度の値Ｃはフィルタ設定手段３８に入力される。
【００８６】
以上の作用と同じ作用が、注目画素設定手段３４が設定する注目画素を順次代えてなされ、入力された全ての画素についての上記集中度の値Ｃがフィルタ設定手段３８に入力される。
【００８７】
フィルタ設定手段３８は、集中度の値Ｃが大きいほど大きな重み付けがなされるような空間フィルタＦ１を設定し、フィルタリング手段３９が設定された空間フィルタにより帯域制限画像信号Ｂ_ｋに対してフィルタリング処理を施して、フィルタ処理済み帯域制限画像信号ＦＢ_ｋを得、これが信号算出手段４０に入力される。
【００８８】
信号算出手段４０は、帯域制限画像信号Ｂ_ｋからフィルタ処理済み帯域制限画像信号ＦＢ_ｋを減算してノイズ帯域制限画像信号ＮＢ_ｋを算出する。
【００８９】
このようにしてノイズ分離手段２２において得られたノイズ帯域制限画像信号ＮＢ_ｋのうち、最低解像度のノイズ帯域制限画像信号ＮＢ_ｎはノイズ信号Ｓｎとされるとともに、ノイズ信号Ｓｎが１段階高解像度のノイズ帯域制限画像信号ＮＢ_ｎ−１と同一画素数となるように、補間処理手段２４において上記補間処理手段１１と同様に補間処理がなされて、拡大ノイズ信号Ｓｎ′が得られる。この後、ノイズ帯域制限画像信号ＮＢ_ｎ−１と拡大ノイズ信号Ｓｎ′とが加算器２５において加算されて、ノイズ信号Ｓｎ−１が得られる。そして、ノイズ信号Ｓｋ−１の補間拡大による拡大ノイズ信号Ｓｋ−１′の取得、および拡大ノイズ信号Ｓｋ−１′とノイズ帯域制限画像信号ＮＢ_ｋ−１との加算によるノイズ信号Ｓｋ−２の取得を繰り返し行なって、最高解像度のノイズ信号Ｓ１を得る。
【００９０】
具体的には図１３に示すように、５段階のノイズ帯域制限画像信号ＮＢ_１〜ＮＢ_５が得られた場合には、まず最低解像度のノイズ帯域制限画像信号ＮＢ_５がノイズ信号Ｓ５とされる。そして、ノイズ信号Ｓ５に対して１段階高解像度のノイズ帯域制限画像信号ＮＢ_４と同一画素数となるように補間処理が施されて拡大ノイズ信号Ｓ５′が得られる。そして、ノイズ帯域制限画像信号ＮＢ_４と拡大ノイズ信号Ｓ５′とが加算されて、ノイズ信号Ｓ４が得られる。以下同様にしてノイズ信号Ｓ３，Ｓ２が得られ、最終的に最高解像度のノイズ信号Ｓ１が得られる。
【００９１】
このようにして最高解像度のノイズ信号Ｓ１が得られると、本発明に係るノイズ除去手段としての演算器２６において、下記の式（８）に示すようにノイズ信号Ｓ１に対して原画像信号Ｓorgの値に応じた、ノイズ成分を除去する度合いを示すパラメータとしての強調係数α（Ｓorg）が乗じられ、さらにこの強調係数α（Ｓorg）が乗じられたノイズ信号Ｓ１’が原画像信号Ｓorgから減算されて、ノイズ成分が低減された画像を表す処理済み画像信号Ｓprocが得られる。
【００９２】
Ｓproc＝Ｓorg−Ｓ１’＝Ｓorg−α（Ｓorg）・Ｓ１ （８）
（但し、Ｓproc：ノイズが除去された画像信号
Ｓorg ：原画像信号
α（Ｓorg）：原画像信号に基づいて定められる強調係数
なお、原画像信号Ｓorgおよびノイズ信号Ｓ１を記憶する記憶手段と、強調係数α（Ｓorg）を演算器２２に対して設定するためのパラメータ設定手段とをさらに備えたものとすれば、強調係数α（Ｓorg）の設定値が変更されたときには、原画像信号およびノイズ信号を読み出し、読み出したノイズ信号に対して変更された強調係数α（Ｓorg）を乗算して再度ノイズ信号Ｓ１’を計算し、再計算したノイズ信号Ｓ１’を読み出した原画像信号Ｓorgから除去することもできる。
【００９３】
次いで、第１の実施形態の動作について説明する。図１４は第１の実施形態の動作を示すフローチャートである。まず、読取装置等から原画像信号Ｓorgが画像処理装置１に入力される（ステップＳ１）。原画像信号Ｓorgは帯域制限画像信号作成手段２に入力されてここで原画像信号Ｓorgの周波数帯域毎の周波数応答特性を表す帯域制限画像信号Ｂ_ｋが作成される（ステップＳ２）。帯域制限画像信号Ｂ_ｋは、上述したようにノイズ成分が分離され（ステップＳ３）、ノイズ帯域制限画像信号ＮＢ_ｋが得られる（ステップＳ４）。この後、ノイズ帯域制限画像信号ＮＢ_ｋ′の１段階高周波数帯域への補間処理によるノイズ信号Ｓｋの取得、およびノイズ信号Ｓｋと対応する周波数帯域のノイズ帯域制限画像信号ＮＢ_ｋとの加算によるノイズ信号Ｓｋ−１の取得を最高周波数帯域のノイズ帯域制限画像信号ＮＢ_１まで繰り返し行なって、ノイズ信号Ｓ１を得る（ステップＳ５）。そしてノイズ信号Ｓ１を用いて上記式（８）に示す演算を行なって処理済み画像信号Ｓprocを得（ステップＳ６）、処理済み画像信号Ｓprocを不図示のモニタに表示する（ステップＳ７）。オペレータは表示された画像を観察し、ノイズ除去の程度を変更する必要があれば（ステップＳ８）、どの程度変更するかを処理手段３に入力する。これにより処理手段３においては上記式（８）における強調係数α（Ｓorg）を変更してステップＳ６に戻り、ステップＳ６からステップＳ８の処理を行なう。ノイズ除去の程度が適切なものとなった場合にはステップＳ８が肯定されて処理を終了する。
【００９４】
このように、本実施形態においては、上記式（８）における強調係数α（Ｓorg）の値を変更するのみで、原画像信号Ｓorgから減算するノイズ信号Ｓ１のレベルを任意に変更することができるため、上記特開平6-96200号に記載された方法と比較して、ノイズ除去の程度を簡易に変更することができ、これにより、ノイズ除去程度が変更された処理済み画像信号Ｓprocを得るための演算時間を短縮して、オペレータのストレスを低減することができる。
【００９５】
なお、上記第１の実施形態においては、ラプラシアンピラミッドの手法により原画像信号Ｓorgから各周波数帯域毎の特性を表す帯域制限画像信号を得ているが、例えば特開平6-274615号に示すように、ウェーブレット変換により帯域制限画像信号を得るようにしてもよい。以下ウェーブレット変換を用いた画像処理の実施形態を第２の実施形態として説明する。
【００９６】
図１５は本発明の第２の実施形態による画像処理装置の構成を示す概略ブロック図である。図１５に示すように、本発明の第２の実施形態による画像処理装置５１は、読取装置等において得られた所定の解像度を有する原画像信号Ｓorgをウェーブレット変換するウェーブレット変換手段５２と、ウェーブレット変換により得られた信号に基づいて、原画像信号Ｓorgのノイズを除去する処理を行なって処理済み画像信号Ｓprocを得る処理手段５３とを有する。
【００９７】
図１６はウェーブレット変換手段５２の構成を示す概略ブロック図である。なお、本実施形態においては、ウェーブレット変換の各係数が直交する直交ウェーブレット変換を行なうものである。
【００９８】
まず、図１６に示すように原画像信号Ｓorgに対してウェーブレット変換部６１においてウェーブレット変換が施される。図１７はウェーブレット変換部６１において行なわれる処理を示すブロック図である。図１７に示すように、原画像信号Ｓorg（信号ＬＬｋ）の主走査方向に基本ウェーブレット関数Ｈ，Ｇによりフィルタリング処理を行なうとともに、主走査方向の画素を１画素おきに間引き（図中↓２で表す）、主走査方向の画素数を１／２にする。ここで、関数Ｈはハイパスフィルタであり、関数Ｇはローパスフィルタである。さらに、この画素が間引かれた信号のそれぞれに対して副走査方向に関数Ｈ，Ｇによりフィルタリング処理を行なうとともに、副走査方向の画素を１画素おきに間引き、副走査方向の画素数を１／２にして、ウェーブレット変換係数信号（以下単に信号とすることもある）ＨＨ１，ＨＬ１，ＬＨ１，ＬＬ１（ＨＨｋ＋１，ＨＬｋ＋１，ＬＨｋ＋１，ＬＬｋ＋１）を得る。ここで、信号ＬＬ１は原画像の縦横を１／２に縮小した画像を表し、信号ＨＬ１、ＬＨ１およびＨＨ１はそれぞれ原画像の１／２縮小画像において縦エッジ、横エッジおよび斜めエッジ成分の画像を表すものとなる。
【００９９】
次に、信号ＬＬ１に対してさらにウェーブレット変換部６１においてウェーブレット変換が施されて、信号ＨＨ２，ＨＬ２，ＬＨ２，ＬＬ２が得られる。ここで、信号ＬＬ２は原画像の縦横を１／４に縮小した画像を表し、信号ＨＬ２、ＬＨ２およびＨＨ２はそれぞれ原画像の１／４縮小画像において縦エッジ、横エッジおよび斜めエッジ成分の画像を表すものとなる。
【０１００】
以下、上記と同様にして、各周波数帯域において得られるウェーブレット変換係数信号ＬＬｋに対するウェーブレット変換をｎ回繰り返すことによりウェーブレット変換係数信号ＨＨ１〜ＨＨｎ，ＨＬ１〜ＨＬｎ，ＬＨ１〜ＬＨｎ，ＬＬ１〜ＬＬｎを得る。ここで、ｎ回目のウェーブレット変換により得られるウェーブレット変換係数信号ＨＨｎ，ＨＬｎ，ＬＨｎ，ＬＬｎは、原画像信号Ｓorgと比較して主副各方向の画素数が(1/2）^ｎ となっているため、各ウェーブレット変換係数信号はｎが大きいほど周波数帯域が低く、原画像データの周波数成分のうち低周波数成分を表すデータとなる。したがって、ウェーブレット変換係数信号ＨＨｋ（ｋ＝０〜ｎ、以下同様）は、原画像信号Ｓorgの主副両方向の周波数の変化を表すものであり、ｋが大きいほど低周波信号となる。またウェーブレット変換係数信号ＨＬｋは原画像信号Ｓorgの主走査方向の周波数の変化を表すものであり、ｋが大きいほど低周波信号となる。さらにウェーブレット変換係数信号ＬＨｋは原画像信号Ｓorg の副走査方向の周波数の変化を表すものであり、ｋが大きいほど低周波信号となる。
【０１０１】
ここで、図１８にウェーブレット変換係数信号を複数の周波数帯域毎に示す。なお、図１８においては便宜上２回目のウェーブレット変換を行った状態までを表すものとする。なお、図１８において信号ＬＬ２は原画像を主副各方向が１／４に縮小した画像を表すものとなっている。
【０１０２】
なお、ウェーブレット変換係数信号ＨＨｋ，ＨＬｋ，ＬＨｋ，ＬＬｋ（ｋ＝１〜ｎ）のうち、信号ＨＨｋ，ＨＬｋ，ＬＨｋはその周波数帯域におけるエッジ成分を表すものであり、換言すれば原画像における特定の周波数帯域（帯域制限画像特性）を有する画像を表すもの、すなわち主にその周波数帯域における画像のコントラストを表すものとなっている。また、ウェーブレット変換係数信号ＬＬｋは上述したように原画像を縮小した画像を表すものとなっている。なお、本実施形態においては、ウェーブレット変換係数信号ＨＨｋ，ＨＬｋ，ＬＨｋを帯域制限画像信号と称し、ウェーブレット変換係数信号ＬＬｋを解像度信号と称し、帯域制限画像信号および解像度信号を総称してウェーブレット変換係数信号と称するものとする。ここで、最低解像度の信号ＬＬｎは上記第１の実施形態と同様にこれ以降の演算において使用することはないため、値を０とする。
【０１０３】
処理手段５３は、上記第１の実施形態における処理手段３と同様にノイズ除去処理を行なうものである。図１９は処理手段５３の構成をウェーブレット変換手段５２とともに示す概略ブロック図である。図１９に示すように、ウェーブレット変換手段５２において得られた帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋ（以下Ｂ_ｋ で代表させる）が、各帯域制限画像信号Ｂ_ｋ に対応するように設けられた各ノイズ分離手段６２に入力される。各ノイズ分離手段６２は上記第１の実施形態におけるノイズ分離手段２２と同様の構成を有するものであり、ここで上記第１の実施形態と同様にノイズ帯域制限画像信号ＮＨＨｋ，ＮＨＬｋ，ＮＬＨｋ（以下ＮＢ_ｋ で代表させる）が得られる。すなわち、帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋを上記第１の実施形態における帯域制限画像信号Ｂ_ｋ と見なすことにより、上記と同様にアイリスフィルタによる集中度の算出、空間フィルタの設定、空間フィルタによるフィルタリング処理およびフィルタリング処理後の信号の帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋからの減算を全ての周波数帯域の帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋについて行なうことにより、ノイズ帯域制限画像信号ＮＨＨｋ，ＮＨＬｋ，ＮＬＨｋを得るものである。
【０１０４】
そして、得られたノイズ帯域制限画像信号ＮＢ_ｋ （ＮＨＨｋ，ＮＨＬｋ，ＮＬＨｋ、ｋ＝１〜ｎ）に対して逆ウェーブレット変換手段６４において逆ウェーブレット変換が施される。図２０は、逆ウェーブレット変換手段６４において行なわれる逆ウェーブレット変換を説明するための図である。図２０に示すように、最低周波数帯域のノイズ帯域制限画像信号ＮＨＨｎ，ＮＨＬｎ，ＮＬＨｎ，ＮＬＬｎ（＝０）に対して逆ウェーブレット変換手段６４において逆ウェーブレット変換を施して処理済み信号ＮＬＬｎ−１を得る。
【０１０５】
図２１は逆ウェーブレット変換手段６４において行なわれる処理を示すブロック図である。図２１に示すようにノイズ帯域制限画像信号ＮＬＬｎ（ＮＬＬｋ，ｋ＝ｎの場合ＮＬＬｎ＝０）およびノイズ帯域制限画像信号ＮＬＨｎ（ＮＬＨｋ）の副走査方向に対して画素間に１画素分の間隔をあける処理を行なうとともに（図中↑２で表す）、関数Ｇ，Ｈに対応する逆ウェーブレット変換関数Ｇ′，Ｈ′によりフィルタリング処理を副走査方向に施してこれらを加算し、さらに加算により得られた信号（第１の加算信号とする）の主走査方向に対して画素間に１画素分の間隔をあける処理を行なうとともに、関数Ｇ′によりフィルタリング処理を主走査方向に施して第１の信号を得る。一方、信号ＮＨＬｎ（ＮＨＬｋ）および信号ＮＨＨｎ（ＮＨＨｋ）の副走査方向に対して画素間に１画素分の間隔をあける処理を行なうとともに、関数Ｇ′，Ｈ′によりフィルタリング処理を副走査方向に施してこれらを加算し、さらに加算により得られた信号（第２の加算信号とする）の主走査方向に対して画素間に１画素分の間隔をあける処理を行なうとともに、関数Ｈ′によりフィルタリング処理を主走査方向に施して第２の信号を得る。そして第１および第２の信号を加算してノイズ帯域制限画像信号ＮＬＬｎ−１（ＮＬＬｋ−１）を得る。なお、最低解像度のウェーブレット変換係数信号ＮＬＬｎは０とされているため、ノイズ帯域制限画像信号ＮＬＬｎ−１は原画像信号Ｓorgの帯域制限画像特性を表すものとなる。
【０１０６】
次に、ノイズ帯域制限画像信号ＮＨＨｎ−１，ＮＨＬｎ−１，ＮＬＨｎ−１，ＮＬＬｎ−１に対して上記と同様に逆ウェーブレット変換手段６４において逆ウェーブレット変換を行なって、ノイズ帯域制限画像信号ＮＬＬｎ−２を得る。そして、以下上記と同様にして逆ウェーブレット変換を最高周波数帯域まで繰り返し、さらにノイズ帯域制限画像信号ＮＨＨ１，ＮＨＬ１，ＮＬＬ１を逆ウェーブレット変換することによりノイズ信号Ｓ１が得られる。
【０１０７】
得られたノイズ信号Ｓ１は、演算器６５において上記第１の実施形態と同様に式（８）に示すような演算が行なわれて処理済み画像信号Ｓprocが得られる。
【０１０８】
次いで、第２の実施形態の動作について説明する。図２２は第２の実施形態の動作を示すフローチャートである。まず、読取装置等から原画像信号Ｓorgが画像処理装置５１に入力される（ステップＳ１１）。原画像信号Ｓorgはウェーブレット変換手段５２においてウェーブレット変換が施されて各周波数帯域毎のウェーブレット変換係数信号が得られる（ステップＳ１２）。各ウェーブレット変換係数信号Ｂ_ｋ はノイズ成分が分離されて（ステップＳ１３）、ノイズ帯域制限画像信号ＮＢ_ｋが得られる（ステップＳ１４）。この後、ノイズ帯域制限画像信号ＮＢ_ｋが、逆ウェーブレット変換手段６４において逆ウェーブレット変換されてノイズ信号Ｓ１が得られる（ステップＳ１５）。そしてノイズ信号Ｓ１を用いて上記式（８）に示す演算を行なって処理済み画像信号Ｓprocを得（ステップＳ１６）、処理済み画像信号Ｓprocを不図示のモニタに表示する（ステップＳ１７）。オペレータは表示された画像を観察し、ノイズ除去の程度を変更する必要があれば（ステップＳ１８）、どの程度変更するかを処理手段５３に入力する。これにより処理手段５３においては上記式（８）における強調係数α（Ｓorg）を変更してステップＳ１６に戻り、ステップＳ１７からステップＳ１８の処理を行なう。ノイズ除去の程度が適切なものとなった場合にはステップＳ１８が肯定されて処理を終了する。
【０１０９】
このように、第２の実施形態においても、上記式（８）における強調係数α（Ｓorg）の値を変更することにより、原画像信号Ｓorgから減算するノイズ信号Ｓ１のレベルを任意に変更することができ、これにより、原画像信号Ｓorgからのノイズ除去の程度を任意に変更することができる。
【０１１０】
なお、上記各実施形態においては、アイリスフィルタを用いて帯域制限画像信号Ｂ_ｋ からノイズ成分を分離しているが、これに限定されるものではなく他の任意の手法によりノイズ成分を分離するようにしてもよい。例えば、帯域制限画像信号Ｂ_ｋに対して所定サイズのマスク内の局所的な分散を求め、この分散が小さな値となる画素をノイズと見なす等してノイズ成分を分離してもよい。また、各帯域制限画像信号により表される各帯域制限画像の各画素における画素ベクトルを算出し、算出した画素ベクトルあるいは該画素ベクトルを修正した修正画素ベクトルを利用してノイズ成分を分離してもよい。以下画素ベクトルあるいは修正画素ベクトルを用いた画像処理の実施形態を第３の実施形態として説明する。
【０１１１】
図２３は本発明の第３の実施形態による画像処理装置の構成を示す概略ブロック図である。この図２３は、上記第２の実施形態における処理手段５３の構成を示した図１９に対応するものであって、ノイズ分離手段６２を有する処理手段５３に代えて、ノイズ分離手段７２を有する処理手段７３を設けている点が異なる。この処理手段７３は、上記第２の実施形態における処理手段５３と同様にノイズ除去処理を行なうものであり、ウェーブレット変換手段６１において得られた帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋ（以下Ｂ_ｋ で代表させる）が、ノイズ分離手段７２に入力される。
【０１１２】
図２４は、各ノイズ分離手段７２の構成を示す概略ブロック図である。この第３の実施形態によるノイズ分離手段７２は、各帯域制限画像信号Ｂ_ｋ により表される各帯域制限画像の各画素における画素ベクトルを各画素毎に算出し、算出した画素ベクトルあるいは該画素ベクトルを修正した修正画素ベクトルを利用してノイズ成分としてのノイズ帯域制限画像信号ＮＢ_ｋ を分離するものであり、図２４に示すように、ウェーブレット変換手段６１において得られた各帯域制限画像信号Ｂ_ｋ から後述するようにして画素ベクトルを算出する画素ベクトル算出手段８２と、画素ベクトル算出手段８２において算出された画素ベクトルを修正する画素ベクトル修正手段８３と、修正された画素ベクトルに基づいてノイズ成分ＮＢ_ｋ を分離して出力する分離手段８５とを備える。
【０１１３】
画素ベクトル算出手段８２においては以下のようにして画素ベクトルが算出される。図２５は画素ベクトルの算出方法を説明するための図である。なお、この画素ベクトルの算出は全周波数帯域の帯域制限画像信号Ｂ_ｋ （ウェーブレット変換係数信号）により表される画像における全画素について行なわれる。ある画素を注目画素（図２５において黒色で示す）とした場合において、注目画素を中心とした７×７の領域を設定する。そしてこの領域内における注目画素近傍の４８画素について、注目画素を中心とした０〜１５の１６方向における一定長さ分（図２５においては３画素分、例えば２の方向における斜線画素）の画素値の平均値を算出し、この平均値と注目画素の画素値との差が最も小さい方向を決定する。なお、図２６に示すように注目画素の近傍８画素を用いて、８方向における注目画素と隣接画素との差を求め、この差が最も小さい方向を決定してもよい。このようにして求められた方向は濃度の傾きが最も小さい方向であり、等信号線すなわち信号勾配の法線方向を向くものである。そして、この方向と上述したように求められた差の逆数を大きさに持つベクトルを画素ベクトルとして求める。したがって、この画素ベクトルは、等信号線の方向において濃度差が小さいほど大きいものとなる。なお、上記差が０の場合は画素ベクトルの大きさは無限大となってしまうため、画素ベクトルの大きさに上限値（例えば８ビットの場合は２５５）を設定することが好ましい。
【０１１４】
なお、上述した平均値と注目画素の画素値との差（あるいは近傍画素と注目画素との差、以下単に差とする）が最も大きい方向は信号勾配方向となり、この方向に画素ベクトルを求めることもできる。この場合、画素ベクトルの大きさは上記差をそのまま用いることができる。なお、本実施形態においては画素ベクトルは等信号線方向を向き、画素ベクトルの大きさは上記差の逆数として説明する。
【０１１５】
画素ベクトル修正手段８３においては以下のようにして画素ベクトルが修正される。原画像信号をウェーブレット変換した場合、比較的高周波数帯域の画像においては詳細なエッジ情報が表現され、中間周波数帯域の画像においては中間周波数帯域のエッジ情報が、低周波数帯域の画像においては低周波数帯域の大きなエッジ情報が表現されることとなる。一般的に各周波数帯域の画像が持っているエネルギは高周波数帯域ほど小さくなるが、ノイズのエネルギは周波数帯域に依存しないという特性があるため、低周波数帯域の画像ほどＳ／Ｎが良好なものとなる。ここで、原画像におけるノイズが混入していない部分（図２７（ａ）参照）は、いずれの帯域制限画像においてもエッジ部分にのみ信号を有することとなるため（図２７（ｂ）〜（ｄ）参照）、比較的高帯域制限画像において、画素ベクトルを求めた画素を含む所定領域における画素値の分散値が小さければ、低周波帯域の画像の画素ベクトルを参照しなくてもその画素ベクトルを求めた注目画素は平坦部にあると見なすことができる。
【０１１６】
一方、原画像におけるノイズが混入した部分（図２８（ａ））は、高帯域制限画像においてはノイズの影響により画素ベクトルの方向が乱されて分散値が大きくなるが（図２８（ｂ））、低周波数帯域となるほど信号に対するノイズの影響が小さくなって分散値が小さくなる（図２８（ｃ）、（ｄ））。したがって、高帯域制限画像において画素ベクトルを求めた注目画素を含む所定領域における画素値の分散値が大きい場合は、低周波数帯域の画像における対応する画素ベクトルを参照しなければ、その画素ベクトルを求めた注目画素が平坦部にあるものであるのかエッジ部分にあるものであるのかが分からない。
【０１１７】
このため、画素ベクトル修正手段８３は、画素ベクトルを求めた注目画素を中心とする例えば３×３の領域内における画素値の分散値を全画素について求め、分散値が同一周波数帯域の画像における他の領域と比較して相対的に小さい場合には、そこは平坦部と見なして画素ベクトルの修正は行なわないで、画素ベクトル算出手段８２において求められた画素ベクトルをそのまま修正画素ベクトルとする。一方、分散値が同一周波数帯域の画像における他の領域と比較して相対的に大きい場合には、そこは平坦部であるかエッジ部分であるか分からないため、低周波帯域の画像における対応する画素の画素ベクトルをその注目画素の修正画素ベクトルとして、その画素ベクトルを求めた注目画素が平坦部にあるかエッジ部分にあるかの確度を向上させるものである。これにより、平坦部の画素ベクトルはより平坦部を表すものとして、エッジ部分の画素ベクトルはよりエッジ部分を表すものとして修正されることとなる。
【０１１８】
なお、画素ベクトル修正手段８３においては、画素ベクトルを算出した際の注目画素とその近傍の画素との差分値を分散値として求めてもよい。この差分値としては、例えば注目画素近傍８画素から画素ベクトルを求めた場合は、注目画素と近傍８画素の差の和、あるいはこの差の平均値等としてもよい。
【０１１９】
分離手段８５においては以下のようにしてノイズ成分が分離される。上記画素ベクトル修正手段８３において求められた修正画素ベクトルが小さい画素については平坦部にあってノイズ成分を含む画素であると見なすことで各周波数帯域の平滑化信号により表される画像の各画素をラベリングする、すなわち判定対象画素がノイズ成分を表す画素であるのか否かを判断する。
【０１２０】
なお、画素ベクトルが小さい場合には、注目画素は平坦部すなわちノイズ成分にあるものと見なせるが、微小エッジ部分にある可能性もある。このため、分離手段８５においては、画素ベクトルが小さい場合には注目画素の画素ベクトルとその近傍画素の画素ベクトルの方向とを参照し、図２９（ａ）に示すように近傍の画素ベクトルが注目画素の画素ベクトルと同一方向を向いている場合にはその注目画素をエッジ成分にあるものと見なし、図２９（ｂ）に示すように近傍の画素ベクトルが注目画素の画素ベクトルと異なる方向を向いている場合には、その注目画素をノイズ成分にあるものと見なすようにすることが好ましい。なお、図２９においては各画素の数字は画素ベクトルの方向（図２６参照）を示すものである。
【０１２１】
そして、このラベリングの結果に基づいて、ノイズ成分にあると見なされた画素については画素値を小さくする平滑化処理が行なわれる。なお、この処理は上述のようにして求められたノイズ成分に関する情報（ラベリングの結果）により、画素そのものを変更する処理すなわち各帯域制限画像における局所的なコントラストを変更するための処理であるため、各周波数帯域における画像のコントラストを表す帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋに対してのみ処理が行なわれ、処理済み帯域制限画像信号ＨＨｋ′，ＨＬｋ′，ＬＨｋ′（ｋ＝１〜ｎ；以下Ｂ_ｋ ′で代表させる）が得られる。そして、各帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋから、対応する処理済み帯域制限画像信号Ｂ_ｋ ′を差し引くことにより、ノイズ成分としてのノイズ帯域制限画像信号ＮＨＨｋ，ＮＨＬｋ，ＮＬＨｋ（すなわちＮＢ_ｋ ＝Ｂ_ｋ −Ｂ_ｋ ′）が分離される。
【０１２２】
以下、上記第２の実施形態と同様に、得られたノイズ帯域制限画像信号ＮＢ_ｋ に対して逆ウェーブレット変換手段６４において逆ウェーブレット変換が最高周波数帯域まで繰り返して行われることによりノイズ信号Ｓ１が得られ、この得られたノイズ信号Ｓ１に基づいて、演算器６５において上記第１の実施形態と同様に式（８）に示すような演算が行なわれて処理済み画像信号Ｓprocが得られる。
【０１２３】
上記第３の実施形態の動作を示すフローチャートを図３０に示す。まず、読取装置等から原画像信号Ｓorg が画像処理装置５１に入力され（ステップＳ２１）、この原画像信号Ｓorg はウェーブレット変換手段５２においてウェーブレット変換が施されて各周波数帯域毎のウェーブレット変換係数信号が得られる（ステップＳ２２）。次に、各ウェーブレット変換係数信号に基づいて画素ベクトル算出手段８２において上述したように画素ベクトルが算出される（ステップ２３）。画素ベクトルの算出の後、画素ベクトル修正手段８３において画素ベクトルが修正されて修正画素ベクトルが求められる（ステップＳ２４）。
【０１２４】
次に分離手段８５において画素ベクトル修正手段８３において求められた修正画素ベクトルに基づいて、ノイズ成分を分離する処理が施されてノイズ帯域制限画像信号ＮＢ_ｋ が分離される（ステップＳ２５）。この後、逆ウェーブレット変換手段６４においてノイズ帯域制限画像信号ＮＢ_ｋ に対して逆ウェーブレット変換が最高周波数帯域まで繰り返し行なわれてノイズ信号Ｓ１が得られる（ステップＳ２６）。そしてノイズ信号Ｓ１を用いて上記式（８）に示す演算を行なって処理済み画像信号Ｓprocを得（ステップＳ２７）、処理済み画像信号Ｓprocに基づく画像を不図示のモニタに表示する（ステップＳ２８）。オペレータは表示された画像を観察し、ノイズ除去の程度を変更する必要があれば（ステップＳ２９）、どの程度変更するかを処理手段７３に入力する。これにより処理手段７３においては上記式（８）における強調係数α（Ｓorg） を変更してステップＳ２７に戻り、ステップＳ２８からステップＳ２９の処理を行なう。ノイズ除去の程度が適切なものとなった場合にはステップＳ２９が肯定されて処理を終了する。
【０１２５】
このように、第３の実施形態においても、上記式（８）における強調係数α（Ｓorg） の値を変更することにより、原画像信号Ｓorg から減算するノイズ信号Ｓ１のレベルを任意に変更することができ、これにより、原画像信号Ｓorg からのノイズ除去の程度を任意に変更することができる。
【０１２６】
また、各周波数帯域において得られる処理済み信号ＨＨｋ′，ＨＬｋ′，ＬＨｋ′，ＬＬｎ′はノイズ成分が低減される処理が施されているため、最終的に得られる処理済み画像信号Ｓprocにおいても、ノイズ成分が低減されたものとなる。したがって、ノイズが目立たなくなった高画質の画像を再現可能な処理済み画像信号Ｓprocを得ることができる。
【０１２７】
次いで、本発明の第４の実施形態について説明する。図３１は本発明の第４の実施形態による画像処理装置の構成を示す概略ブロック図である。
【０１２８】
この図３１は、上記第３の実施形態におけるノイズ分離手段７２の構成を示した図２４に対応するものであって、分離手段８５に代えて、平滑化手段８４を設けている点が異なる。なお、ウェーブレット変換手段６１、画素ベクトル算出手段８２、画素ベクトル修正手段８３、および逆ウェーブレット変換手段６４は上記第３の実施形態におけるものと同一であるため、ここでは詳細な説明は省略する。
【０１２９】
平滑化手段８４は、画素ベクトル修正手段８３において修正された修正画素ベクトルに基づいてウェーブレット変換係数信号に対して以下のようにして平滑化処理を施して平滑化信号を得るものである。なお、平滑化処理は各周波数帯域の帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋに対して行なわれる。図３２は平滑化手段８４における平滑化処理を説明するための図である。注目画素を中心とする３×３の領域において各画素の画素値が図３２（ａ）に示す値を有する場合、画素ベクトル（修正画素ベクトル）は図３２（ｂ）に示すものとなる。そして、図３２（ｂ）に斜線で示すように注目画素と画素ベクトル方向にある画素および画素ベクトル方向と反対方向にある画素を用いて平滑化フィルタによりフィルタリングを行なう。ここで、平滑化フィルタとしては、方向性を持っているフィルタであればどのようなフィルタを用いてもよく、例えば図３３（ａ）に示す平均値フィルタや図３３（ｂ）に示す平滑化フィルタを用いることができる。図３３（ａ）に示す平均値フィルタを用いた場合、図３２（ａ）に示す画素値は図３４（ａ）に示すように平滑化されて注目画素の画素値は１０１となる。また、図３３（ｂ）に示す平滑化フィルタを用いた場合、図３４（ｂ）に示すように注目画素の画素値は１４１となるように平滑化される。このように平滑化を行なうことにより、例えばエッジ上にノイズが混入されている場合に、そのノイズを目立たなくすることができる。また、エッジ上でなくとも平坦部において平滑化を行なうことにより、平坦部にあるノイズを目立たなくすることができる。なお、平滑化された帯域制限画像信号（ウェーブレット変換係数信号）を平滑化信号（平滑化帯域制限画像信号）とする。
【０１３０】
なお、ここでは画素ベクトル方向にある画素および画素ベクトル方向と反対方向にある画素を用いて平滑化を行なっているが、画素ベクトル方向にある画素のみを用いて平滑化を行なってもよい。この場合、図３２（ａ）に示す注目画素は９９（＝（１０１＋９８）／２）の値を有するように平滑化される。
【０１３１】
また、注目画素の近傍４８画素から画素ベクトルを求めた場合において、画素ベクトルの方向が図３５に示す方向であった場合には、図３５の斜線に示すように注目画素および画素ベクトル方向の画素（さらには画素ベクトルと反対方向の画素）を用いて平滑化を行えばよい。具体的には、図３５に示す斜線で示す全７画素における画素値の平均値を注目画素の画素値とすればよい。
【０１３２】
このようにして各周波数帯域の平滑化信号が求められたら、平滑化前の帯域制限画像信号ＨＨｋ，ＨＬｋ，ＬＨｋから、対応する平滑化信号を差し引くことにより、ノイズ帯域制限画像信号ＮＨＨｋ，ＮＨＬｋ，ＮＬＨｋ（すなわちＮＢ_ｋ ＝Ｂ_ｋ −Ｂ_ｋ ′）を分離する。
【０１３３】
以下、上記第３の実施形態と同様に、得られたノイズ帯域制限画像信号ＮＢ_ｋ に対して逆ウェーブレット変換手段６４において逆ウェーブレット変換が最高周波数帯域まで繰り返して行われることによりノイズ信号Ｓ１が得られ、この得られたノイズ信号Ｓ１に基づいて、演算器６５において上記第１の実施形態と同様に式（８）に示すような演算が行なわれて処理済み画像信号Ｓprocが得られる。
【０１３４】
図３６は第４の実施形態の動作を示すフローチャートである。図３６に示すように、まず、原画像信号Ｓorg に対してウェーブレット変換手段６１においてウェーブレット変換が行なわれて各周波数帯域毎のウェーブレット変換係数信号が得られる（ステップＳ３１）。次に、各ウェーブレット変換係数信号に基づいて画素ベクトル算出手段８２において上述したように画素ベクトルが算出される（ステップＳ３２）。画素ベクトルの算出の後、画素ベクトル修正手段８３において画素ベクトルが修正されて修正画素ベクトルが求められる（ステップＳ３３）。そして、修正画素ベクトルに基づいて、各ウェーブレット変換係数信号に対して平滑化手段８４において平滑化処理が施されて平滑化信号が得られた後（ステップＳ３４）、該平滑化信号に基づいてノイズ成分を分離する処理が施されてノイズ帯域制限画像信号ＮＢ_ｋ が分離される（ステップＳ３５）。以下第３の実施の形態の動作を示すフローチャート（図３０）に示すステップＳ２７〜２９の処理を行なう。
【０１３５】
ここで、原画像にノイズが混入している場合、画像中のエッジ成分にもノイズが含まれることとなる。この場合、画素ベクトルに基づいてノイズ成分を分離してエッジ成分を強調すると、エッジ成分に含まれるノイズをも強調してしまうこととなる。本実施形態においては、画素ベクトルや修正画素ベクトルの方向に基づいて平滑化手段８４において平滑化処理を行っているため、エッジ成分を失うことなくエッジ上のノイズ成分を抽出でき、またエッジ以外の平坦部のノイズも抽出できるので、最終的には、エッジ上のノイズが目立たなくなるとともに、平坦部におけるノイズも目立たなくなり、高画質の画像を再現できる。
【０１３６】
なお、画素ベクトルの大きさに基づいて、平滑化帯域制限画像信号のノイズ成分およびエッジ成分を分離した後、平滑化帯域制限画像信号に対して、ノイズ成分に対する平滑化処理および／またはエッジ成分に対する強調処理を施して処理済帯域制限画像信号を得、該処理済帯域制限画像信号を用いて平滑化前の帯域制限画像信号に含まれるノイズ信号を取得するようにすれば、エッジ上のノイズを目立たせることなくエッジ強調を行なうことができ、また平坦部のノイズを一層低減することができるので、一層高画質の画像を再現することもできる。
【０１３７】
以上、本発明の好ましい実施の形態について説明したが、本発明は上記実施の形態に限定されるものではなく、発明の要旨を変更しない限りにおいて、種々変更することが可能である。
【０１３８】
例えば、上記各実施形態においては、上記式（８）においてノイズ信号Ｓ１に乗算する強調係数を原画像信号Ｓorg の関数としているが、これに限定されるものではなく、定数としてもよい。
【０１３９】
また、上記第３および第４の実施形態においては、画素ベクトル算出手段８２において、注目画素の画素値とその近傍画素の画素値の平均値（あるいは近傍画素の画素値）との差が最も小さい方向を画素ベクトルの方向として求めているが、上記差が２番目に小さい方向を第２の画素ベクトルとして求めてもよい。あるいは信号勾配方向に画素ベクトルを求める場合には、上記差が２番目に大きい方向を第２の画素ベクトルとして求めてもよい。このように第２の画素ベクトルを求めることにより、例えば図３７（ａ）に示すようにエッジ成分が屈曲して存在する場合においては図３７（ｂ）に示すように２つの画素ベクトルが求められる。そして、平滑化手段８４において第１および第２の画素ベクトルの双方を用いて平滑化を行なうことにより、エッジ成分をその方向性を維持してより正確に平滑化することができる。
【０１４０】
また、画像に含まれる比較的大きなエッジは低周波帯域の画像においても残るが、ノイズについては低周波帯域の画像ほど小さくなるものである。このため、ある周波数帯域における帯域制限画像の一の画素における画素ベクトルの方向を、低周波数帯域の画像における一の画素に対応する画素の画素ベクトルの方向と同じにすることにより、その画素がエッジ成分にある場合はその画素ベクトルはよりエッジ成分を表すものとなる。一方、その画素がノイズ成分にある場合は低周波帯域の画像の方が細かなノイズが小さくなることから、画素ベクトルはランダムな方向を向きかつ大きさはさらに小さくなるため、その画素ベクトルはより平坦部すなわちノイズ成分を表すものとなる。したがって、上記第３および第４の実施形態においては、画素ベクトル修正手段８３において、上記分散値に基づく処理に代えてある周波数帯域のある画素における画素ベクトルの方向を、さらに低周波数帯域の画像における上記ある画素に対応する画素の画素ベクトルの方向と同じになるように修正することにより、その画素がエッジ成分にあるかノイズ成分にあるかの確度を向上させることができる。特に第３の実施形態においては、これにより分離手段８５におけるノイズ成分およびエッジ成分の分離を正確に行なうことができる。
【０１４１】
さらに、上記第３および第４の実施形態においては、画素ベクトル修正手段８３において画素ベクトルを修正しているが、画素ベクトル算出手段８２において算出された画素ベクトルをそのまま用いて、分離手段８５はノイズ成分の分離を行ない、平滑手段８４は平滑化を行なうものとしていもよい。
【０１４２】
また、例えば人体の胸部のように軟部および骨部から構成された被写体に互いにエネルギの異なる放射線を照射して複数の放射線画像を得、これら複数の放射線画像を読み取ってこれら複数の放射線画像のそれぞれを表す複数の画像信号を得、これら複数の画像信号に基づいてエネルギーサブトラクション処理を行なって被写体の主として軟部が記録された軟部画像を表す軟部画像信号もしくは被写体の主として骨部が記録された骨部画像を表す骨部画像信号を求め、求められた軟部画像もしくは骨部画像を観察の対象とする場合がある。この場合において、軟部画像もしくは骨部画像のノイズ成分を低減するために、骨部画像信号に対して平滑化処理を施して第１の平滑化画像信号を求め、原画像信号から第１の平滑化画像信号を減算することにより軟部画像を表す軟部画像信号を求める第１の処理を行ない、さらに軟部画像信号に対して平滑化処理を施して第２の平滑化画像信号を求め、原画像信号から第２の平滑化画像信号を減算することにより、ノイズが除去された骨部画像信号を求める第２の処理を行ない、上記第１および第２の処理を繰り返すことにより、ノイズ成分を低減するようにしたエネルギーサブトラクション画像生成方法が提案されている（例えば特開平5-236351号）。ここで、このようなエネルギーサブトラクション画像生成方法において、平滑化画像を求める際に、本発明による処理を施すようにしてもよいものである。このように、エネルギーサブトラクション画像生成方法において、本発明による処理を施すことによって平滑化画像信号を求めることにより、ノイズ成分のみが低減されてエッジ成分を目立つものとすることができ、これにより高画質の軟部画像もしくは骨部画像を得ることができる。
【０１４３】
また、上記第２〜第４の実施形態においては、原画像信号Ｓorg に対してウェーブレット変換を施すことにより得られる信号に対して、上述したような画素ベクトルに基づく処理を施しているが、ウェーブレット変換のみならずラプラシアンピラミッド等、原画像信号Ｓorg を多重解像度変換する手法において得られる周波数帯域毎の帯域制限画像信号に対しても、上記と同様に処理を施すことができる。
【図面の簡単な説明】
【図１】本発明の第１の実施形態による画像処理装置の構成を示す概略ブロック図
【図２】原画像の主走査方向および副走査方向を示す図
【図３】帯域制限画像信号作成処理の概要を示すブロック図
【図４】帯域制限画像信号作成処理を模式的に示す図
【図５】フィルタリング処理に使用されるフィルタの一例を示す図
【図６】低解像度画像信号作成処理の詳細を示す図
【図７】補間処理に使用されるフィルタの一例を示す図
【図８】処理手段の構成を帯域制限画像信号作成手段とともに示す概略ブロック図
【図９】ノイズ分離手段の構成を示す概略ブロック図
【図１０】アイリスフィルタを示す概念図
【図１１】アイリスフィルタにおける勾配ベクトルを算出するマスクを示す図
【図１２】空間フィルタの算出を説明するための図
【図１３】変換処理を模式的に示す図
【図１４】第１の実施形態の動作を示すフローチャート
【図１５】本発明の第２の実施形態による画像処理装置の構成を示す概略ブロック図
【図１６】ウェーブレット変換手段の構成を示す概略ブロック図
【図１７】ウェーブレット変換部において行なわれる処理を示すブロック図
【図１８】ウェーブレット変換係数信号を複数の周波数帯域毎に示す図
【図１９】処理手段の構成を帯域制限画像信号作成手段とともに示す概略ブロック図
【図２０】逆ウェーブレット変換を説明するための図
【図２１】逆ウェーブレット変換手段において行なわれる処理を示すブロック図
【図２２】第２の実施形態の動作を示すフローチャート
【図２３】本発明の第３の実施形態による画像処理装置に使用される処理手段の構成を帯域制限画像信号作成手段とともに示す概略ブロック図
【図２４】本発明の第３の実施形態による画像処理装置に使用されるノイズ分離手段の構成を示す概略ブロック図
【図２５】画素ベクトルの算出を説明するための図（その１）
【図２６】画素ベクトルの算出を説明するための図（その２）
【図２７】ウェーブレット変換係数信号を示す図（その１）
【図２８】ウェーブレット変換係数信号を示す図（その２）
【図２９】分離手段における画素ベクトルの参照結果を示す図
【図３０】第３の実施形態の動作を示すフローチャート
【図３１】本発明の第４の実施形態による画像処理装置の構成を示す概略ブロック図
【図３２】平滑化処理を説明するための図（その１）
【図３３】平滑化フィルタの一例を示す図
【図３４】平滑化された画素値を示す図
【図３５】平滑化処理を説明するための図（その２）
【図３６】第４の実施形態の動作を示すフローチャート
【図３７】平滑化処理の他の例を説明するための図
【符号の説明】
１，５１ 画像処理装置
２ 帯域制限画像信号作成手段
３，５３，７３ 処理手段
１０ フィルタリング処理手段
１１，２４ 補間処理手段
１２ 減算器
２２，６２，７２ ノイズ分離手段
２５ 加算器
２６，６５ 演算器
５２ ウェーブレット変換手段
６１ ウェーブレット変換部
６４ 逆ウェーブレット変換手段[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing method and apparatus for performing processing for removing noise on an original image signal, and a computer-readable recording medium on which a program for causing a computer to execute the image processing method is recorded.
[0002]
[Prior art]
In the field of image processing, wavelet transform and Laplacian pyramid techniques are used as means for classifying image signals for each frequency band when different image processing is performed on the image signal for each frequency band. . Examples of the image processing include high-frequency separation for noise removal, and compression processing by reducing data in a frequency band with a lot of noise. The present applicant has also proposed various image processing methods that perform image processing such as emphasizing only edge components in an image using wavelet transform (for example, Japanese Patent Laid-Open Nos. H6-274615 and H6- 350989).
[0003]
On the other hand, a method called Laplacian pyramid is described in, for example, Japanese Patent Application Laid-Open No. 5-244508, Japanese Patent Application Laid-Open No. 6-96200, Japanese Patent Application Laid-Open No. 6-301766, and the Laplacian pyramid is approximated by a Gaussian function with respect to the original image. After performing mask processing with such a mask, the image is sub-sampled, and the number of pixels is thinned out to halve to obtain a blurred image that is 1/4 the size of the original image. The pixel having a value of 0 is interpolated to return to the original size image, and this image is further subjected to mask processing with the mask described above to obtain a blurred image, and this blurred image is subtracted from the original image, A band-limited image signal (bandpass signal) representing a frequency component of a certain limited frequency band of the original image signal, that is, a frequency response characteristic for each of a plurality of frequency bands of the original image is obtained. By repeating this process for the blurred image obtained, 1/2 of the original image is obtained. ^2N N band-limited image signals having the following size are generated. The blurred image in the lowest frequency band represents the low frequency component of the original image.
[0004]
Here, in the radiographic image, the quantum noise of the radiation becomes conspicuous in the portion where the radiation dose is low and the density is low. For this reason, in the above Japanese Patent Laid-Open No. 6-96200, the radiographic image is decomposed into a plurality of frequency band images by a Laplacian pyramid to obtain a band limited image signal for each frequency band, and the local dispersion value of each band limited image signal And processing for removing noise components from the band-limited image signal according to the size of the local dispersion value, and applying the processing to the band-limited image signal that represents the blurred image in the lowest frequency band. There has been proposed a method for obtaining a processed image signal in which a noise component corresponding to a frequency band in an image is reduced by reconstructing the image signal together with the image signal.
[0005]
[Problems to be solved by the invention]
In the method described in the above Japanese Patent Laid-Open No. 6-96200, since the noise component is removed from the band limited image signal in each frequency band, the noise is excessively removed or the degree of noise removal Is insufficient, it is necessary to reconstruct the image after changing the degree of noise removal with respect to the band limited image signal again. As a result, reprocessing takes a long time. Therefore, when an image with appropriate noise removal is obtained by variously changing the degree of noise removal while observing the CRT, it takes a long time to reproduce the processed image. The operator's stress is great.
[0006]
The present invention has been made in view of the above circumstances, and is an image processing method and apparatus capable of appropriately removing noise components from an original image, and a computer-readable recording medium storing a program for causing a computer to execute the image processing method. The object is to provide a recording medium.
[0007]
[Means for Solving the Problems]
An image processing method according to the present invention is an image processing method for performing image processing for reducing noise of an original image on an original image signal representing an original image. A noise signal having the same number of pixels as the original image signal is obtained based on the created band-limited image signal, and noise components of the original image signal are removed based on the noise signal It is.
[0008]
Here, “the same number of pixels as the original image signal” means that the image size of the image represented by the original image signal and the noise image represented by the noise signal are the same.
[0009]
Further, “removing the noise component of the original image signal based on the noise signal” means that the noise component included in the reproduced image is reduced. For example, the noise component from the original image signal, Alternatively, the noise component of the original image signal may be removed by subtracting the noise component multiplied by a coefficient representing a predetermined noise suppression level. The coefficient may be changed depending on the signal value of the original image signal.
[0010]
In the image processing method according to the present invention, a band-limited image signal is generated by performing multi-resolution conversion on the original image signal, a noise component is separated from the band-limited image signal to obtain a noise band-limited image signal, and the noise The noise signal can be obtained by inverse-multiresolution conversion of the band limited image signal. The inverse multi-resolution conversion corresponds to the multi-resolution conversion, and the original signal can be restored (reversible / irreversible) by performing the inverse multi-resolution conversion. It goes without saying that it is a thing.
[0011]
Here, when “creating a band-limited image signal by performing multi-resolution conversion of the original image signal”, the frequency of the original image signal for each of a plurality of frequency bands is determined by Laplacian pyramid decomposition using the Laplacian pyramid method or by wavelet transform. For example, a method of converting to a signal representing response characteristics can be used. In this case, as “inverse multi-resolution conversion”, a Laplacian pyramid reconstruction method is used when a band-limited image signal is obtained by Laplacian pyramid decomposition, and an inverse wavelet transform is used when a band-limited image signal is obtained by wavelet conversion. Needless to say, is used.
[0012]
Further, the “noise band limited image signal” refers to a signal that represents only a noise component included in the band limited image signal.
[0013]
In the image processing method according to the present invention, it is preferable that the noise signal is acquired based on a filtering process using an iris filter.
[0014]
In the image processing method according to the present invention, the pixel vector in each pixel of the band limited image represented by the band limited image signal can be calculated, and the noise signal can be acquired based on the pixel vector.
[0015]
Here, the “pixel vector” represents the inclination direction and the magnitude of the inclination of the pixel value of the target pixel when a pixel in the frequency band image is the target pixel. When calculating the “pixel vector”, for example, with respect to a plurality of directions centered on the target pixel, a pixel value of the target pixel and a pixel value of a pixel in the vicinity thereof (the neighboring pixels are a plurality of pixels in a certain direction). In this case, a difference from the average value) is obtained, a direction in which the difference is the largest or smallest is determined, and a pixel vector is calculated based on the direction and the difference.
[0016]
Here, when the direction with the largest difference is a pixel vector, the pixel vector indicates the direction of the signal gradient, and when the direction with the smallest difference is the pixel vector, the pixel vector indicates the direction of the equal signal line. It becomes. When the pixel vector is obtained in the direction of the signal gradient, if the magnitude is the difference between the pixel value of the target pixel and its neighboring pixels, the pixel for which the pixel vector is obtained is in the edge component as the pixel vector increases. As the pixel vector is smaller, the pixel for which the pixel vector is obtained can be regarded as being in a flat portion. Conversely, when the pixel vector is obtained in the direction of the signal gradient, if the magnitude is the reciprocal of the difference between the pixel values of the pixel of interest and its neighboring pixels, the smaller the pixel vector, the more the pixel for which the pixel vector is obtained. As the pixel vector is larger, the pixel for which the pixel vector is obtained can be regarded as being in the flat part.
[0017]
Furthermore, when the pixel vector is obtained in the direction of the equal signal line, if the size is the difference between the pixel value of the target pixel and its neighboring pixels, the smaller the pixel vector is, the more the pixel for which the pixel vector is obtained is in the edge component. As the pixel vector increases, the pixel for which the pixel vector is obtained can be regarded as being in a flat portion. Conversely, when the pixel vector is obtained in the direction of the equal signal line, if the size is the reciprocal of the difference between the pixel values of the pixel of interest and its neighboring pixels, the larger the pixel vector, the more the pixel for which the pixel vector was obtained is an edge. As the pixel vector is smaller, the pixel for which the pixel vector is obtained can be regarded as being in the flat part.
[0018]
In addition, as the direction of the pixel vector, two types of directions, that is, a direction having the largest difference and a direction having the second largest difference, or a direction having the smallest difference and a direction having the second smallest difference may be obtained. The pixel vector consists of two vectors.
[0019]
Further, when a pixel vector is obtained in the direction of the iso-signal line for a certain target pixel and the magnitude of the pixel vector is the reciprocal of the difference, as described above, the larger the pixel vector is, the more the target pixel is at the edge. Is smaller, the pixel of interest can be regarded as being in a flat portion, and the pixel can be regarded as noise in the flat portion.
[0020]
Based on the above, when “acquiring a noise signal based on a pixel vector”, it is determined whether the pixel is at an edge or a flat part according to the direction and / or size of the pixel vector. It can be seen that a noise signal should be obtained (a noise component is separated) from the band-limited image signal according to the determination result.
[0021]
In the case of “acquiring a noise signal from a band limited image signal”, for example, the noise component and the edge component of the band limited image signal are separated based on the size of the pixel vector, A processed band limited image signal is obtained by performing a smoothing process on the noise component and / or an emphasis process on the edge component, and a noise signal included in the band limited image signal before smoothing is processed using the processed band limited image signal. Get it. Here, the “smoothing process for the noise component” is a process for reducing the pixel value of the pixel corresponding to the noise component, and the “enhancement process for the edge component” is increased by the pixel value of the pixel corresponding to the edge component. It is processing.
[0022]
In addition, when acquiring a noise signal based on a pixel vector in this way, a pixel vector (peripheral pixel vector) in a pixel near each pixel is calculated, and the noise signal is also calculated based on the calculated peripheral pixel vector. It is preferable to obtain.
[0023]
Further, when calculating a pixel vector, a pixel vector in one pixel of a band-limited image in one frequency band is set to a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. It is desirable to perform correction based on the pixel vector and obtain a noise signal based on the corrected pixel vector instead of the pixel vector.
[0024]
Here, “correcting the pixel vector” means that the direction of the pixel vector of one pixel in one frequency band is the direction of the pixel vector of the pixel corresponding to one pixel in a frequency band lower than the one frequency band. To match.
[0025]
Further, when calculating a pixel vector, a variance value of a predetermined region including one pixel of a band-limited image in one frequency band is calculated, and the pixel vector of the one pixel is corrected based on the variance value. If it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector in the one pixel is changed to the one pixel in the image in a frequency band lower than the one frequency band. It is desirable to correct based on the pixel vector of the corresponding pixel and obtain a noise signal based on the modified pixel vector instead of the pixel vector.
[0026]
Here, the “dispersion value” may be not only the dispersion value of the predetermined area, but also a difference value between the pixel of interest when the pixel vector is calculated and the neighboring pixels. In addition, as the difference value, for example, when a pixel vector is obtained from eight pixels near the target pixel, a sum of differences between the target pixel and the eight neighboring pixels, or an average value of the difference may be used.
[0027]
In addition, “determining whether or not to correct the pixel vector of the one pixel based on the variance value” means that when the variance value of a predetermined area including a certain pixel is smaller than the variance value in another area If the dispersion value is large, it is determined to refer to the low frequency band image without considering the low frequency band image as a flat portion.
[0028]
In addition, as described above, “correcting a pixel vector” means that the direction of the pixel vector of one pixel in one frequency band is the pixel of the pixel corresponding to one pixel in a frequency band lower than the one frequency band. Matching the direction of the vector.
[0029]
When “correcting the pixel vector”, the peripheral pixel vector may be corrected.
[0030]
In the image processing method according to the present invention, the band limited image signal is smoothed based on the pixel vector to obtain a smoothed band limited image signal, and the noise is calculated based on the smoothed band limited image signal instead of the pixel vector. A signal can also be acquired.
[0031]
In this case, it is desirable to obtain a smoothed band limited image signal based on the pixel vector corrected by using the above-described methods instead of the pixel vector.
[0032]
Here, “smoothing the band-limited image signal based on the pixel vector” means that the noise contained in the edge component (noise on the edge) is preserved while the edge component is stored based on the direction of the pixel vector, particularly the pixel vector. This means that the band-limited image signal is smoothed so as to be suppressed. For example, when the pixel vector is a vector in the equal signal line direction, smoothing may be performed using the pixel of interest for which the pixel vector has been obtained, a pixel in the vector direction, and a pixel on the opposite side of the vector direction. In “smoothing”, a method of obtaining an average value of pixel values of pixels in the pixel vector direction, a method of smoothing using a smoothing filter, or the like can be used.
[0033]
In addition, when “acquiring a noise signal based on a smoothed band-limited image signal”, the noise component contained in the band-limited image signal before smoothing is separated using the smoothed band-limited image signal to obtain the noise signal. Any method may be used as long as it is, for example, a noise signal (noise band limited image signal) may be obtained by subtracting the smoothed band limited image signal from the band limited image signal before smoothing. Further, after separating the noise component and the edge component of the smoothed band limited image signal based on the size of the pixel vector, the smoothing process for the noise component and / or the edge component is performed on the smoothed band limited image signal. A processed band limited image signal may be obtained by performing enhancement processing, and a noise signal included in the band limited image signal before smoothing may be acquired using the processed band limited image signal. As described above, “smoothing processing for noise components” is processing for reducing the pixel values of pixels corresponding to noise components, and “enhancement processing for edges components” is processing for increasing the pixel values of pixels corresponding to edge components. It is.
[0034]
In the image processing method according to the present invention, the original image signal and the noise signal are stored, and when the setting value of the parameter indicating the degree of removal of the noise component is changed, the original image signal and the noise signal are read out and read out. It is desirable to remove the noise component of the original image signal based on the original image signal and the noise signal and the changed parameters.
[0035]
An image processing apparatus according to the present invention is a band-limited image that creates a band-limited image signal from an original image signal in an image processing apparatus that performs image processing for reducing noise of the original image on an original image signal representing the original image. A signal generation unit; a noise signal acquisition unit that obtains a noise signal having the same number of pixels as the original image signal based on the band-limited image signal; and a noise component of the original image signal is removed based on the noise signal. And a noise removing means.
[0036]
In the image processing apparatus according to the present invention, the band-limited image signal creating unit creates the band-limited image signal by performing multi-resolution conversion of the original image signal, and the noise signal obtaining unit is configured from the band-limited image signal. A noise signal can be obtained by separating a noise component to obtain a noise band limited image signal and performing inverse multi-resolution conversion on the noise band limited image signal. Here, the multi-resolution conversion can be conversion by Laplacian pyramid decomposition or wavelet conversion.
[0037]
Furthermore, the noise signal acquisition means of the image processing apparatus according to the present invention is preferably means for acquiring the noise signal by filtering processing using an iris filter.
[0038]
The image processing apparatus according to the present invention further includes pixel vector calculation means for calculating a pixel vector in each pixel of the band limited image represented by the band limited image signal, and the noise signal acquisition means includes the pixel It is desirable to acquire a noise signal based on the pixel vector calculated by the vector calculation means.
[0039]
When the pixel vector calculating unit is provided as described above, it is preferable that the noise signal acquiring unit acquires a noise signal based on a pixel vector in a pixel near each pixel.
[0040]
Further, when the pixel vector calculating means is provided, the pixel vector in one pixel of the band limited image in one frequency band is set as the one pixel in the image in the lower frequency band than the one frequency band. And a noise signal acquisition means for acquiring a noise signal based on the corrected pixel vector instead of the pixel vector. Is desirable.
[0041]
Further, in the case where the pixel vector calculating unit is provided, a variance value calculating unit that calculates a variance value of a predetermined region including one pixel of the band limited image in one frequency band, and based on the variance value A determination means for determining whether or not to correct the pixel vector of the one pixel; and when it is determined to correct the pixel vector of the one pixel, the pixel vector in the one pixel is set to the one frequency band. Correction means for correcting based on the pixel vector of the pixel corresponding to the one pixel in the lower frequency band image, and the noise signal acquisition means is corrected instead of the pixel vector It is desirable to obtain a noise signal based on the pixel vector.
[0042]
Further, in the case where the pixel vector calculating means is provided, the smoothing means for obtaining the smoothed band limited image signal by smoothing the band limited image signal based on the pixel vector and the noise signal The acquisition unit may acquire a noise signal based on the smoothed band limited image signal.
[0043]
In this case, it is desirable that the smoothing unit obtains a smoothed band limited image signal based on the pixel vector corrected by the correcting unit instead of the pixel vector.
[0044]
In the image processing apparatus of the present invention, the first storage means for storing the original image signal and the second storage means (first storage means) for storing the noise signal acquired by the noise signal acquisition means. And a parameter setting means for setting a parameter indicating the degree of removal of the noise component to the noise removing means,
When the parameter setting value is changed by the parameter setting unit, the noise removing unit reads the original image signal from the first storage unit and reads the noise signal from the second storage unit. It is desirable to generate a denoised image signal based on the noise signal and the changed parameters.
[0045]
The image processing method according to the present invention may be provided by being recorded on a computer-readable recording medium as a program for causing a computer to execute the image processing method.
[0046]
【The invention's effect】
According to the present invention, a band limited image signal is generated from an original image signal, a noise signal having the same number of pixels as the original image signal is obtained based on the generated band limited image signal, and the original image signal is converted based on the noise signal. Since the noise component is removed, the noise component corresponding to the frequency band of the original image signal can be removed.
[0047]
The original image signal and the generated noise signal are stored, and when the setting value of the parameter indicating the degree to which the noise component is removed is changed, the original image signal and the noise signal are read, and the read original image signal and noise signal are read If the noise component of the original image signal is removed on the basis of the changed parameter and the changed parameter, the noise signal is stored once when the noise signal is output again based on the noise-removed signal after the noise signal is created once. Therefore, the degree of noise removal from the original image signal can be arbitrarily changed simply by changing the parameter and changing the level of the noise signal. Compared with the method described in Kaihei 6-96200, the degree of noise removal can be changed easily. There was shortened operation time for obtaining the modified processed image signal, it is possible to reduce the operator's stress.
[0048]
In addition, various methods can be used as a method for generating the band limited image signal. For example, the band limited image signal can have the original image size. As a result, the original image can be smoothed with a mask of a plurality of sizes, a band-limited image signal of the original image size can be obtained, and a noise signal can be generated by adding all the noise band-limited image signals that have been subjected to noise separation. Of course, multi-resolution conversion can be used as a method of generating the band limited image signal.
[0049]
Also, various methods can be used as a noise signal acquisition method. For example, a pixel vector in each pixel of the band limited image represented by the band limited image signal is calculated, and a noise component (noise signal) is separated based on the pixel vector. Here, as described above, the situation differs depending on whether the pixel vector is obtained in the equal signal line direction or the signal gradient direction, or as the difference or the inverse of the difference. When the pixel vector is obtained in the signal line direction and the magnitude of the pixel vector is the reciprocal of the above difference, the pixel vector is large in the edge portion, and the pixel vector is small in the flat portion, that is, the noise portion. Therefore, when this method is used, the noise component of the band limited image can be separated according to the size of the pixel vector. For example, after performing a smoothing process for reducing the pixel value of the pixel with respect to the separated noise component, the noise signal is separated from the band-limited image signal based on the signal subjected to the smoothing process. The noise component corresponding to the frequency band of the original image signal can be removed based on the noise signal.
[0050]
Here, when the pixel vector is obtained in the equal signal line direction and the size of the pixel vector is the reciprocal of the above difference, if the value of the pixel vector is relatively small, the pixel for which the pixel vector is obtained is flat. It can be considered that there is a part, that is, noise, but there is a possibility that it is at a minute edge in the image. On the other hand, when the pixel is at the edge, the pixel vector in the neighboring pixel is directed in the same direction, and when it is noise, the pixel vector in the neighboring pixel is directed in a random direction. Therefore, it is possible to improve the accuracy of whether a certain pixel represents an edge or noise by also based on a pixel vector of a neighboring pixel of each pixel, thereby more accurately determining a noise component. Can be separated.
[0051]
In addition, a relatively large edge included in the original image remains in the low frequency band image, but the noise becomes smaller in the low frequency band image. Therefore, by making the pixel vector in one pixel of the band limited image in one frequency band the same as the pixel vector direction of the pixel corresponding to one pixel in the image in the frequency band lower than the one frequency band. When the pixel is in the edge component, the pixel vector more represents the edge component. On the other hand, if the pixel is in the noise component, the finer noise will be smaller in the low frequency band image, so the pixel vector will be in a random direction and the size will be smaller, so the pixel vector will be flatter Part, that is, a noise component. Therefore, by correcting the pixel vector in one pixel of the band-limited image in one frequency band based on the pixel vector of the pixel corresponding to one pixel in the image in the lower frequency band than the one frequency band, It is possible to improve the accuracy of whether a pixel is an edge or noise, and thereby it is possible to more accurately separate a noise component and an edge component.
[0052]
Further, when the original image signal is subjected to multi-resolution conversion, detailed edge information is expressed in a relatively high frequency band image, and in the intermediate frequency band image, the edge information in the intermediate frequency band is expressed in the low frequency band image. Represents large edge information in the low frequency band. Generally, the energy of a frequency band image decreases as the frequency band increases, but the noise energy does not depend on the frequency band. Therefore, the image of the low frequency band has a better S / N. Become. Here, the portion in which noise is not mixed in the original image (see FIG. 27A) has a signal only in the edge portion in any band limited image (FIGS. 27B to 27D). If the variance of the pixel values in the predetermined region including the pixel for which the pixel vector is obtained is small in the relatively high frequency band image, the pixel vector can be referred to without referring to the pixel vector of the low frequency band image. It can be considered that the target pixel for which is obtained is in a flat portion.
[0053]
On the other hand, the portion of the original image mixed with noise (FIG. 28 (a)) has a large dispersion value due to the disturbance of the direction of the pixel vector due to the influence of noise in the high frequency band image (FIG. 28 (b)). ), The lower the frequency band, the smaller the influence of noise on the signal and the smaller the dispersion value (FIGS. 28C and 28D). Accordingly, when the variance of pixel values in a predetermined region including one pixel for which a pixel vector is obtained in one band-limited image is large, the pixel vector must be referred to unless the corresponding pixel vector in the low-frequency band image is referred to. It is not known whether the pixel for which the value is obtained is in the flat part or in the edge part. Therefore, when the variance value is large in one band-limited image, the flat portion is obtained by matching the pixel vector with the pixel vector of the corresponding pixel in the low-frequency band image with reference to the low-frequency band image. This pixel vector is corrected to represent a flat portion, and the pixel vector of an edge portion is corrected to represent an edge portion. Therefore, based on the corrected pixel vector, the noise component can be more accurately separated.
[0054]
On the other hand, when noise is mixed in the image, the edge component in the image also includes noise. Therefore, the band limited image signal is smoothed based on the pixel vector or the pixel vector (direction) corrected using the above-described methods to obtain a smoothed band limited image signal, and the smoothing is performed instead of the pixel vector. If a method for obtaining a noise signal based on a band-limited image signal is used, noise on the edge can be extracted without losing the edge component, and noise on a flat portion other than the edge can also be extracted. Noise becomes inconspicuous and noise in the flat portion also becomes inconspicuous.
[0055]
Further, after separating the noise component and the edge component of the smoothed band limited image signal based on the size of the pixel vector, the smoothing process for the noise component and / or the edge component is performed on the smoothed band limited image signal. By performing enhancement processing to obtain a processed band limited image signal, and using the processed band limited image signal to obtain a noise signal included in the band limited image signal before smoothing, noise on the edge is reduced. Edge emphasis can be performed without making it conspicuous, and noise on the flat portion can be further reduced, so that a higher quality image can be reproduced.
[0056]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, an image processing method and apparatus according to an embodiment of the present invention will be described in detail with reference to the drawings. The image processing apparatus shown below performs noise removal processing on an image signal obtained by reading a radiation image of a human body recorded on a stimulable phosphor sheet, and the processed image signal is mainly applied to a film. Recorded and used for diagnosis.
[0057]
FIG. 1 is a schematic block diagram showing the configuration of the image processing apparatus according to the first embodiment of the present invention. The image processing apparatus 1 is a band-limited image signal creating unit that creates a band-limited image signal representing frequency response characteristics for each of a plurality of frequency bands of an original image from an original image signal Sorg having a predetermined resolution obtained by a reading device or the like. 2 and processing means 3 for obtaining a processed image signal Sproc by performing a process for removing noise on the original image signal Sorg based on the band-limited image signal.
[0058]
In this embodiment, for example, in the radiation image information recording / reproducing system using the stimulable phosphor sheet recorded in JP-A-55-12492 and JP-A-56-11395, the stimulable phosphor sheet is used. A radiographic image of the human body recorded in the above is read as a digital image signal by laser beam scanning. As shown in FIG. 2, the radiographic image is read by scanning the sheet 10 in the sub-scanning direction (vertical direction) while scanning the stimulable phosphor sheet 10 in the main scanning direction (lateral direction, X direction). , Y direction) and the sheet 10 is scanned two-dimensionally.
[0059]
First, the band limited image signal creation process will be described in detail. FIG. 3 is a block diagram showing an outline of the band-limited image creation process, and FIG. 4 is a diagram schematically showing the band-limited image signal creation process. In this embodiment, a band-limited image signal is created by the Laplacian pyramid method described in, for example, Japanese Patent Application Laid-Open Nos. 5-244508 and 6-96200.
[0060]
First, as shown in FIG. 3, the band-limited image signal creating means 2 in FIG. The image signal L having a lower resolution than the original image signal Sorg ₁ (Hereinafter referred to as a low resolution image signal), and then this low resolution image signal L ₁ The same filtering process is applied to the low resolution image signal L ₁ Low-resolution image signal L whose resolution is even lower than ₂ After that, the same filtering process is sequentially repeated, and the low resolution image signal L for each resolution is generated. _k (K = 1 to n) is obtained. Then, in the interpolation processing means 11, the low resolution image signal L obtained at each stage of the filtering process. _k On the other hand, interpolation processing is performed so that the number of pixels is doubled to obtain a plurality of blurred image signals Sus1 to Susn (hereinafter represented by Susk (k = 1 to n)) having different sharpness. Thereafter, the low-resolution image signal L having the number of pixels corresponding to each other by the subtractor 12. _k-1 And the difference between the blurred image signal Susk and the original image signal Sorg and the blurred image signal Sus1, and this is obtained as the band limited image signal B. _k And
[0061]
In the present embodiment, a filter substantially corresponding to a one-dimensional Gaussian distribution is used as the filter for the filtering process. That is, the filter coefficient of the filter is determined according to the following equation (1) regarding the Gaussian signal.
[0062]
[Expression 1]

This is because the Gaussian signal has good localization in both frequency space and real space. For example, a 5 × 1 one-dimensional filter in the case where σ = 1 in the above equation (1) is as shown in FIG. It will be something.
[0063]
As shown in FIG. 6, the filtering process is performed on the original image signal Sorg or on the low resolution image signal every other pixel. By performing such filtering processing every other pixel in the x direction and the y direction, the low resolution image signal L ₁ The number of pixels of is 1/4 of the original image, and n low-resolution image signals L obtained by repeatedly performing this filtering process on the low-resolution image signal obtained by the filtering process. _k (K = 1 to n) each has half the number of pixels of the original image signal Sorg ^2k Image signal.
[0064]
Next, the low-resolution image signal L thus obtained is _k Interpolation processing to be performed on will be described. Various methods such as a B-spline method can be used as the interpolation calculation method for performing the interpolation processing. In this embodiment, since the low-pass filter based on the Gaussian signal is used in the filtering processing, the interpolation calculation is performed. A Gaussian signal is also used for. Specifically, in the following formula (2), σ = 2 ^k-1 Is used.
[0065]
[Expression 2]

For example, the low resolution image signal L ₁ When k is interpolated, σ = 1 because k = 1. In this case, the filter for performing the interpolation process is a 5 × 1 one-dimensional filter as shown in FIG. This interpolation processing is performed first by the low resolution image signal L ₁ The low resolution image signal L is obtained by interpolating one pixel at a time every other pixel. ₁ Is expanded to have the same number of pixels as the original image, and then this interpolated low-resolution image signal L ₁ On the other hand, the filtering process is performed by the one-dimensional filter shown in FIG.
[0066]
Similarly, this interpolation enlargement process is performed for all the low resolution image signals L _k To. Low resolution image signal L _k Is interpolated based on the above equation (2), 3 × 2 ^k -1 filter is created and the low resolution image signal L _k By interpolating one pixel having a value of 0 between each pixel, a low-resolution image signal L having a one-step high resolution _k-1 The low-resolution image signal L is enlarged so that the number of pixels becomes the same, and the pixel whose value is 0 is interpolated. _k 3x2 for ^k A blurred image signal Susk is obtained by performing interpolation with a filter having a length of −1 and performing interpolation.
[0067]
Next, the blurred image signal Susk created as described above has a low resolution image signal L having a corresponding number of pixels. _k-1 Subtracted from the band-limited image signal B _k (K = 1 to n) is obtained. Band-limited image signal B _k Is as shown in the following formula (3).
[0068]
B ₁ = Sorg-Sus1
B ₂ = L ₁ -Sus2
B ₃ = L ₂ -Sus3
・ (3)
・
B _k = L _k -Susk
More specifically, as shown in FIG. ₁ ~ L ₅ Is obtained, the low-resolution image signal L having the lowest resolution is first obtained. ₅ Is subjected to interpolation processing to obtain a low resolution image signal L ₄ A blurred image signal Sus5 having the same number of pixels as is generated. And the low resolution image signal L ₄ The blurred image signal Sus5 is subtracted from the band-limited image signal B ₅ Get. Sequentially L ₃ -Sus4, L ₂ -Sus3, L ₁ -Sus2, Sorg-Sus1 are calculated, and the band limited image signal B ₁ ~ B ₅ Get. Here, the lowest resolution low resolution image signal L _k (L ₅ ) Represents low-frequency information obtained by reducing the original image, but is not used in subsequent calculations.
[0069]
Next, the band limited image signal B calculated as described above. _k The process performed using is described. FIG. 8 is a schematic block diagram showing the configuration of the processing means 3 together with the band limited image signal creating means 2. As shown in FIG. 8, the band limited image signal B generated by the band limited image signal generating means 2 is used. _k The noise component is separated in the noise separation means 22 as the noise signal generation means of the present invention, and the noise band limited image signal NB _k Is obtained. Here, the noise component separation processing in the noise separation means 22 will be described.
[0070]
FIG. 9 is a schematic block diagram showing the configuration of the noise separating means 22. The noise separation means 22 performs band-limited image signal B by processing using an iris filter. _k The noise component is separated from the input band-limited image signal B. _k For all the pixels of the band-limited image signal B _k The gradient vector calculation means 31 for calculating the signal gradient vector for each pixel based on the above, the attention pixel setting means 34 for sequentially setting one of all the pixels as the attention pixel, and the attention set by the attention pixel setting means 34 Direction line setting means 33 for setting a plurality of (for example, 32) radial direction lines (see FIG. 10) adjacent to each other at a predetermined angular interval (for example, 11.25 degree interval) around the pixel, and each set direction line For each pixel on the direction line within a predetermined range from the target pixel, an angle θil formed by the gradient vector of each pixel and the direction in which the direction line extends (i th of 32 direction lines) Index value calculation means for respectively obtaining an index value cosθil based on the gradient vector of the l-th pixel from the target pixel and the direction in which the i-th direction line extends. 5 and an average value Ci (n) of the index values cosθil of each pixel on the direction line within the range from the start point to the end point by changing the end point to a size corresponding to the preset range with the target pixel as the start point Is obtained for each direction line according to the following equation (4), and the maximum value calculating means 36 for extracting the maximum value Cimax (equation (5)) out of the average value is extracted for each direction line. Concentration calculation means for calculating the concentration value C (expression (6)) of the gradient vector group for this pixel of interest by averaging the maximum value Cimax for all 32 direction lines ((ΣCimax) / 32). 37,
[Equation 3]

[Expression 4]

The larger the concentration value C, the greater the weighting as the pixel of interest is located on the edge in the image, and the smaller the concentration value C, the smaller the weighting as the pixel of interest exists in a portion other than the edge. Further, the band-limited image signal B is set by a filter setting unit 38 for setting a filter coefficient of the spatial filter and a spatial filter for which the filter coefficient is set by the filter setting unit 38 _k The filtered band-limited image signal FB after filtering processing _k Filtering means 39 for obtaining the band-limited image signal B _k To filtered band-limited image signal FB _k To subtract the noise band limited image signal NB _k And signal calculation means 40 for calculating.
[0071]
As for the iris filter, see “Obata et al., Detection of mass shadow in DR image (iris filter), IEICE Transactions D-II Vol.J75-D-II No.3 P663 ～ 670 March 1992” The details are described in “Obata et al., Iris filter and its characteristic analysis, Transactions of the Society of Instrument and Control Engineers, 1998 VOL.34 No.4, p.326-332”. The iris filter processing has been studied as an effective technique for detecting a mass shadow, which is one of the characteristic forms particularly in breast cancer. The iris filter used for the iris filter processing is used to determine the gradient of an image signal as a gradient vector. And the degree of concentration of the gradient vector is output, and the iris filter process is to detect a mass shadow based on the degree of concentration of the gradient vector. In the present embodiment, the band-limited image signal B is obtained by the iris filter processing. _k The degree to which each pixel is located on a line segment such as an edge is obtained from the degree of concentration of the gradient vector.
[0072]
For example, the gradient vector calculating means 31 uses the image data (pixel value) of the pixel at the outermost periphery of the mask having a size of 5 pixels × 5 pixels shown in FIG. To obtain the direction θ of the signal gradient vector.
[0073]
[Equation 5]

This mask size is not limited to 5 pixels vertical by 5 pixels horizontal, and masks of various sizes can be used.
[0074]
The number of direction lines set by the direction line setting means 33 is not limited to the above 32, but if it is set too much, the load required for the calculation process increases rapidly, and if it is too small, the edge component is detected accurately. Since it is not possible, about 32 is preferable. In addition, it is convenient in calculation processing and the like that the angular intervals between the direction lines are equal.
[0075]
The concentration degree value C calculated by the concentration degree calculation means 37 is a large value when the direction of the gradient vector is concentrated on the target pixel.
[0076]
In the filter setting means 38, the filter coefficient of the spatial filter for performing the smoothing process is set according to the concentration value C obtained by the concentration degree calculating means 37. That is, the pixel of interest exists on the edge as the concentration value C increases, and the pixel of interest exists at a position other than the edge as the concentration value C decreases. Therefore, a filter coefficient is set so that the weighting increases as the concentration value C increases, and the spatial filter is set.
[0077]
Specifically, first, binarization processing is performed in which the value of a pixel having a concentration level C higher than a predetermined threshold is 1, and the value of a pixel equal to or lower than the predetermined threshold is set to 0, whereby an edge component and other components are processed. And separation. Here, assuming that the basic spatial filter is F0, this spatial filter F0 is a 3 × 3 smoothing filter, and its filter coefficient is as shown in FIG. The filter coefficient of the filter F0 is weighted according to the conversion result, and the band limited image signal B _k The filter coefficient of the spatial filter F1 to be applied to is set. That is, when a certain pixel of interest is on an edge component and the concentration C (after binarization) in a 3 × 3 range centered on this pixel of interest is as shown in FIG. The filter coefficient of F1 is as shown in FIG. Further, when a certain pixel of interest is in a portion other than the edge component, and the degree of concentration C in the 3 × 3 range centering on the pixel of interest is as shown in FIG. 12D, the filter coefficient of the spatial filter F1 is This is as shown in FIG. Therefore, the band-limited image signal B is obtained by the spatial filter F1. _k Is smoothed, the edge component is not blurred, but in the direction in which it exists. In addition, components other than the edge component have a value of 0.
[0078]
The filtering unit 39 uses the spatial filter F1 set in the filter setting unit 38 to perform the band limited image signal B. _k The filtered band-limited image signal FB after filtering processing _k Get. By this filtering process, the band-limited image signal B _k Is smoothed, but the edge component is smoothed in the direction in which the edge exists. As a result, the filtered band-limited image signal FB _k In, only the smoothed edge component remains.
[0079]
In the signal calculation means 40, the band limited image signal B _k To filtered band-limited image signal FB _k To subtract the noise band limited image signal NB _k Is obtained. Here, the filtered band-limited image signal FB _k Is smoothed, the noise band limited image signal NB _k Is the band-limited image signal B _k It represents the noise component contained in. In particular, the filtered band-limited image signal FB _k Is smoothed in the direction in which the edge component exists, the obtained noise component includes noise on the edge.
[0080]
Band-limited image signal B input to the noise separation means 22 _k Are first input to the gradient vector calculation means 31, the target pixel setting means 34, the filtering means 39, and the signal calculation means 40. As described above, the gradient vector calculation means 31 uses the image data (pixel value) of the pixel at the outermost periphery of the mask having a size of 5 vertical pixels × 5 horizontal pixels to determine the direction θ of the signal gradient vector for all pixels. . The obtained direction θ of the signal gradient vector is input to the index value calculation means 35.
[0081]
On the other hand, the pixel-of-interest setting means 34 receives the input band-limited image signal B _k , One of all the pixels is sequentially set as a target pixel, and the set target pixel is input to the direction line setting means 33. The direction line setting means 33 sets 32 radial direction lines adjacent to each other at equal intervals of, for example, 11.25 degrees with the target pixel as the center. The set direction line is input to the index value calculation means 35.
[0082]
The index value calculation means 35 is a band limited image signal B in which the direction θ of the gradient vector input from the gradient vector calculation means 31 is defined. _k The 32 direction lines input from the direction line setting means 33 are overlapped on the same two-dimensionally arranged pixels, and pixels that respectively overlap the 32 direction lines are extracted.
[0083]
Further, the index value calculating means 35 is configured to determine the angle θil (the i-th direction line out of 32 direction lines) between the direction θ of the gradient vector defined for each pixel for each direction line and the direction in which the direction line extends. , Representing the angle between the gradient vector of the l-th pixel from the target pixel and the direction in which the i-th direction line extends).
[0084]
The obtained index value cosθil of each pixel on each direction line is input to the maximum value calculation means 36. The maximum value calculating means 36 obtains the average value Ci (n) of the index values cosθil of the pixels on the direction line within the range from the start point to the end point for each direction line, and calculates the average value Ci (n). The maximum value Cimax is extracted.
[0085]
Thus, the maximum value Cimax obtained for each direction line is input to the concentration degree calculating means 37. The concentration degree calculation means 37 calculates the concentration value C of the gradient vector group for the pixel of interest by averaging the maximum values Cimax obtained for each input direction line. The calculated concentration value C of the gradient vectors is input to the filter setting means 38.
[0086]
The same operation as the above is performed by sequentially replacing the pixel of interest set by the pixel-of-interest setting unit 34, and the concentration value C for all the input pixels is input to the filter setting unit 38.
[0087]
The filter setting unit 38 sets a spatial filter F1 that is weighted more as the concentration value C is larger, and the band limited image signal B is set by the spatial filter in which the filtering unit 39 is set. _k Is subjected to filtering processing to obtain a filtered band-limited image signal FB _k This is input to the signal calculation means 40.
[0088]
The signal calculation means 40 is configured to output the band limited image signal B _k To filtered band-limited image signal FB _k To subtract the noise band limited image signal NB _k Is calculated.
[0089]
The noise band limited image signal NB obtained by the noise separating means 22 in this way. _k Among them, noise band limited image signal NB with the lowest resolution _n Is a noise signal Sn, and the noise signal Sn is a one-step high resolution noise band limited image signal NB. _n-1 In the interpolation processing unit 24, the interpolation processing is performed in the same manner as the interpolation processing unit 11 so that the expanded noise signal Sn ′ is obtained. After this, the noise band limited image signal NB _n-1 And the expanded noise signal Sn ′ are added by the adder 25 to obtain a noise signal Sn−1. Then, acquisition of the enlarged noise signal Sk-1 ′ by interpolation expansion of the noise signal Sk-1, and the enlarged noise signal Sk-1 ′ and the noise band limited image signal NB _k-1 The noise signal Sk-2 is repeatedly obtained by adding the above to obtain the highest resolution noise signal S1.
[0090]
Specifically, as shown in FIG. 13, the noise band limited image signal NB in five stages ₁ ~ NB ₅ Is obtained, the noise band limited image signal NB having the lowest resolution is first obtained. ₅ Is the noise signal S5. Then, the noise band limited image signal NB having a one-step high resolution with respect to the noise signal S5. ₄ Is subjected to an interpolation process so as to obtain the same number of pixels, and an enlarged noise signal S5 'is obtained. The noise band limited image signal NB ₄ And the enlarged noise signal S5 ′ are added to obtain a noise signal S4. Thereafter, noise signals S3 and S2 are obtained in the same manner, and finally a noise signal S1 having the highest resolution is obtained.
[0091]
When the noise signal S1 having the highest resolution is obtained in this way, the arithmetic unit 26 as the noise removing means according to the present invention obtains the original image signal Sorg with respect to the noise signal S1 as shown in the following equation (8). The enhancement coefficient α (Sorg) as a parameter indicating the degree of removal of the noise component according to the value is multiplied, and the noise signal S1 ′ multiplied by the enhancement coefficient α (Sorg) is further subtracted from the original image signal Sorg. Thus, a processed image signal Sproc representing an image with reduced noise components is obtained.
[0092]
Sproc = Sorg−S1 ′ = Sorg−α (Sorg) · S1 (8)
(However, Sproc: Image signal from which noise has been removed.
Sorg: Original image signal
α (Sorg): Enhancement coefficient determined based on the original image signal
Note that if the storage means for storing the original image signal Sorg and the noise signal S1 and the parameter setting means for setting the enhancement coefficient α (Sorg) to the computing unit 22 are further provided, the enhancement coefficient α. When the set value of (Sorg) is changed, the original image signal and the noise signal are read out, and the read out noise signal is multiplied by the changed enhancement coefficient α (Sorg) to calculate the noise signal S1 ′ again. The recalculated noise signal S1 ′ can be removed from the read original image signal Sorg.
[0093]
Next, the operation of the first embodiment will be described. FIG. 14 is a flowchart showing the operation of the first embodiment. First, an original image signal Sorg is input to the image processing apparatus 1 from a reading device or the like (step S1). The original image signal Sorg is input to the band-limited image signal creating means 2 where the band-limited image signal B representing the frequency response characteristic for each frequency band of the original image signal Sorg. _k Is created (step S2). Band-limited image signal B _k As described above, the noise component is separated (step S3), and the noise band limited image signal NB _k Is obtained (step S4). After this, the noise band limited image signal NB _k 'To obtain a noise signal Sk by interpolation processing into a one-step high frequency band, and a noise band limited image signal NB in a frequency band corresponding to the noise signal Sk _k Acquisition of the noise signal Sk-1 by addition to the noise band limited image signal NB in the highest frequency band ₁ Until the noise signal S1 is obtained (step S5). Then, the processed image signal Sproc is obtained by performing the calculation shown in the above equation (8) using the noise signal S1 (step S6), and the processed image signal Sproc is displayed on a monitor (not shown) (step S7). The operator observes the displayed image, and if it is necessary to change the degree of noise removal (step S8), the operator inputs the degree of change to the processing means 3. As a result, the processing means 3 changes the enhancement coefficient α (Sorg) in the above equation (8), returns to step S6, and performs the processing from step S6 to step S8. If the degree of noise removal is appropriate, step S8 is affirmed and the process is terminated.
[0094]
As described above, in the present embodiment, the level of the noise signal S1 subtracted from the original image signal Sorg can be arbitrarily changed only by changing the value of the enhancement coefficient α (Sorg) in the above equation (8). Therefore, compared with the method described in the above Japanese Patent Laid-Open No. 6-96200, it is possible to easily change the degree of noise removal, thereby obtaining a processed image signal Sproc in which the degree of noise removal is changed. Thus, the operator's stress can be reduced.
[0095]
In the first embodiment, a band-limited image signal representing the characteristics of each frequency band is obtained from the original image signal Sorg by the Laplacian pyramid technique. For example, as shown in Japanese Patent Application Laid-Open No. 6-24615. The band limited image signal may be obtained by wavelet transform. Hereinafter, an embodiment of image processing using wavelet transform will be described as a second embodiment.
[0096]
FIG. 15 is a schematic block diagram showing a configuration of an image processing apparatus according to the second embodiment of the present invention. As shown in FIG. 15, an image processing apparatus 51 according to the second embodiment of the present invention includes a wavelet transform unit 52 for performing wavelet transform on an original image signal Sorg having a predetermined resolution obtained by a reading device, and a wavelet transform. And processing means 53 for obtaining a processed image signal Sproc by performing a process for removing noise of the original image signal Sorg based on the signal obtained by the above.
[0097]
FIG. 16 is a schematic block diagram showing the configuration of the wavelet transform means 52. In the present embodiment, orthogonal wavelet transform in which the coefficients of wavelet transform are orthogonal is performed.
[0098]
First, as shown in FIG. 16, the wavelet transform unit 61 applies wavelet transform to the original image signal Sorg. FIG. 17 is a block diagram showing processing performed in the wavelet transform unit 61. As shown in FIG. 17, filtering is performed by basic wavelet functions H and G in the main scanning direction of the original image signal Sorg (signal LLk), and pixels in the main scanning direction are thinned out every other pixel (↓ 2 in the figure). And the number of pixels in the main scanning direction is halved. Here, the function H is a high-pass filter, and the function G is a low-pass filter. Further, filtering processing is performed on each of the signals from which the pixels are thinned out by the functions H and G in the sub-scanning direction, the pixels in the sub-scanning direction are thinned out every other pixel, and the number of pixels in the sub-scanning direction is set to 1. / 2 to obtain wavelet transform coefficient signals (hereinafter simply referred to as signals) HH1, HL1, LH1, LL1 (HHk + 1, HLk + 1, LHk + 1, LLk + 1). Here, the signal LL1 represents an image obtained by reducing the length and width of the original image to ½, and the signals HL1, LH1, and HH1 respectively represent the image of the vertical edge, the horizontal edge, and the diagonal edge component in the ½ reduced image of the original image. To represent.
[0099]
Next, wavelet transformation is further performed on the signal LL1 in the wavelet transformation unit 61, and signals HH2, HL2, LH2, and LL2 are obtained. Here, the signal LL2 represents an image obtained by reducing the length and width of the original image to ¼, and the signals HL2, LH2, and HH2 respectively represent the image of the vertical edge, the horizontal edge, and the diagonal edge component in the ¼ reduced image of the original image. To represent.
[0100]
In the same manner as described above, wavelet transform coefficient signals HH1 to HHn, HL1 to HLn, LH1 to LHn, and LL1 to LLn are obtained by repeating wavelet transform for the wavelet transform coefficient signal LLk obtained in each frequency band n times. Here, the wavelet transform coefficient signals HHn, HLn, LHn, and LLn obtained by the n-th wavelet transform have (1/2) the number of pixels in each of the main and sub directions compared to the original image signal Sorg. ⁿ Therefore, each wavelet transform coefficient signal has a lower frequency band as n is larger, and becomes data representing a low frequency component among the frequency components of the original image data. Therefore, the wavelet transform coefficient signal HHk (k = 0 to n, hereinafter the same) represents a change in frequency in both the main and sub directions of the original image signal Sorg, and becomes a lower frequency signal as k increases. The wavelet transform coefficient signal HLk represents a change in the frequency in the main scanning direction of the original image signal Sorg, and becomes a lower frequency signal as k is larger. Further, the wavelet transform coefficient signal LHk represents a change in frequency in the sub-scanning direction of the original image signal Sorg, and becomes a lower frequency signal as k is larger.
[0101]
Here, FIG. 18 shows a wavelet transform coefficient signal for each of a plurality of frequency bands. In FIG. 18, for the sake of convenience, the state up to the second wavelet transform is shown. In FIG. 18, the signal LL2 represents an image obtained by reducing the original image to 1/4 in the main and sub directions.
[0102]
Of the wavelet transform coefficient signals HHk, HLk, LHk, and LLk (k = 1 to n), the signals HHk, HLk, and LHk represent edge components in the frequency band, in other words, specific signals in the original image. It represents an image having a frequency band (band-limited image characteristics), that is, mainly represents an image contrast in the frequency band. The wavelet transform coefficient signal LLk represents an image obtained by reducing the original image as described above. In the present embodiment, the wavelet transform coefficient signals HHk, HLk, and LHk are referred to as band-limited image signals, the wavelet transform coefficient signal LLk is referred to as a resolution signal, and the band-limited image signal and the resolution signal are collectively referred to as wavelet transform coefficients. It shall be called a signal. Here, since the signal LLn having the lowest resolution is not used in the subsequent calculations as in the first embodiment, the value is set to 0.
[0103]
The processing unit 53 performs noise removal processing in the same manner as the processing unit 3 in the first embodiment. FIG. 19 is a schematic block diagram showing the configuration of the processing means 53 together with the wavelet transform means 52. As shown in FIG. 19, the band limited image signals HHk, HLk, LHk (hereinafter referred to as B) obtained by the wavelet transform means 52 _k Each band-limited image signal B). _k Is input to each noise separating means 62 provided so as to correspond to. Each noise separation means 62 has the same configuration as the noise separation means 22 in the first embodiment. Here, as in the first embodiment, the noise band limited image signals NHHk, NHLk, NLHk (hereinafter referred to as noise band limited image signals). NB _k To be represented). That is, the band limited image signals BHH, HLk, and LHk are used as the band limited image signal B in the first embodiment. _k As described above, the calculation of the degree of concentration by the iris filter, the setting of the spatial filter, the filtering process by the spatial filter, and the subtraction of the band-limited image signals HHk, HLk, and LHk after the filtering process at all frequencies The noise band limited image signals NHHk, NHLk, and NLHk are obtained by performing the band limited image signals HHk, HLk, and LHk of the band.
[0104]
Then, the obtained noise band limited image signal NB _k The inverse wavelet transform unit 64 performs inverse wavelet transform on (NHHk, NHLk, NLHk, k = 1 to n). FIG. 20 is a diagram for explaining the inverse wavelet transform performed in the inverse wavelet transform unit 64. As shown in FIG. 20, the inverse wavelet transform unit 64 performs inverse wavelet transform on the noise band limited image signals NHHn, NHLn, NLHn, and NLLn (= 0) in the lowest frequency band to obtain a processed signal NLLn-1. .
[0105]
FIG. 21 is a block diagram showing processing performed in the inverse wavelet transform means 64. As shown in FIG. 21, an interval of one pixel is set between pixels in the sub-scanning direction of the noise band limited image signal NLLn (NLLn = 0 when NLLk, k = n) and the noise band limited image signal NLHn (NLHk). In addition to performing processing (represented by ↑ 2 in the figure), filtering processing is performed in the sub-scanning direction by inverse wavelet transform functions G ′ and H ′ corresponding to the functions G and H, and these are added, and further obtained by addition. The first signal is obtained by performing a process of spacing one pixel between pixels in the main scanning direction of the received signal (referred to as the first addition signal) and performing a filtering process in the main scanning direction by the function G ′. Get. On the other hand, processing is performed to leave an interval of one pixel between pixels in the sub-scanning direction of the signals NHLn (NHLk) and the signal NHHn (NHHk), and filtering processing is performed in the sub-scanning direction using the functions G ′ and H ′. These are added, and further, a process is performed to leave an interval of one pixel between the pixels in the main scanning direction of the signal obtained by the addition (referred to as a second addition signal), and a filtering process is performed using the function H ′. In the main scanning direction to obtain a second signal. Then, the first and second signals are added to obtain a noise band limited image signal NLLn-1 (NLLk-1). Since the wavelet transform coefficient signal NLLn with the lowest resolution is set to 0, the noise band limited image signal NLLn-1 represents the band limited image characteristics of the original image signal Sorg.
[0106]
Next, the inverse wavelet transform unit 64 performs inverse wavelet transform on the noise band limited image signals NHHn-1, NHLn-1, NLHn-1, and NLLn-1 in the same manner as described above to obtain the noise band limited image signal NLLn-. Get 2. Then, the inverse wavelet transform is repeated until the highest frequency band in the same manner as described above, and the noise signal S1 is obtained by performing inverse wavelet transform on the noise band limited image signals NHH1, NHL1, and NLL1.
[0107]
The obtained noise signal S1 is subjected to calculation as shown in Expression (8) in the calculator 65 in the same manner as in the first embodiment to obtain a processed image signal Sproc.
[0108]
Next, the operation of the second embodiment will be described. FIG. 22 is a flowchart showing the operation of the second embodiment. First, an original image signal Sorg is input to the image processing device 51 from a reading device or the like (step S11). The original image signal Sorg is subjected to wavelet transform in the wavelet transform means 52, and a wavelet transform coefficient signal for each frequency band is obtained (step S12). Each wavelet transform coefficient signal B _k The noise component is separated (step S13), and the noise band limited image signal NB _k Is obtained (step S14). After this, the noise band limited image signal NB _k However, the inverse wavelet transform means 64 performs inverse wavelet transform to obtain a noise signal S1 (step S15). Then, the processed image signal Sproc is obtained by performing the calculation shown in the above equation (8) using the noise signal S1 (step S16), and the processed image signal Sproc is displayed on a monitor (not shown) (step S17). The operator observes the displayed image, and if it is necessary to change the degree of noise removal (step S18), the operator inputs the degree of change to the processing means 53. As a result, the processing means 53 changes the enhancement coefficient α (Sorg) in the above equation (8), returns to step S16, and performs the processing from step S17 to step S18. If the degree of noise removal is appropriate, step S18 is affirmed and the process ends.
[0109]
As described above, also in the second embodiment, the level of the noise signal S1 to be subtracted from the original image signal Sorg can be arbitrarily changed by changing the value of the enhancement coefficient α (Sorg) in the above equation (8). Accordingly, the degree of noise removal from the original image signal Sorg can be arbitrarily changed.
[0110]
In each of the above embodiments, the band-limited image signal B is obtained using an iris filter. _k However, the present invention is not limited to this, and the noise component may be separated by any other method. For example, the band limited image signal B _k Alternatively, a local variance in a mask of a predetermined size may be obtained, and a noise component may be separated by regarding a pixel having a small variance as noise. Further, a pixel vector in each pixel of each band limited image represented by each band limited image signal is calculated, and noise components are separated using the calculated pixel vector or a corrected pixel vector obtained by correcting the pixel vector. Good. Hereinafter, an embodiment of image processing using a pixel vector or a modified pixel vector will be described as a third embodiment.
[0111]
FIG. 23 is a schematic block diagram showing the configuration of an image processing apparatus according to the third embodiment of the present invention. FIG. 23 corresponds to FIG. 19 showing the configuration of the processing means 53 in the second embodiment, and the processing having the noise separating means 72 instead of the processing means 53 having the noise separating means 62. The difference is that means 73 is provided. The processing means 73 performs noise removal processing in the same manner as the processing means 53 in the second embodiment, and the band limited image signals HHk, HLk, LHk (hereinafter referred to as B) obtained in the wavelet transform means 61. _k ) Is input to the noise separating means 72.
[0112]
FIG. 24 is a schematic block diagram showing the configuration of each noise separating means 72. The noise separation means 72 according to the third embodiment is configured to use each band limited image signal B _k A pixel vector in each pixel of each band limited image represented by the above is calculated for each pixel, and a noise band limited image signal NB as a noise component is calculated using the calculated pixel vector or a modified pixel vector obtained by correcting the pixel vector. _k As shown in FIG. 24, each band limited image signal B obtained by the wavelet transform unit 61 is separated. _k As described later, a pixel vector calculation means 82 for calculating a pixel vector, a pixel vector correction means 83 for correcting the pixel vector calculated by the pixel vector calculation means 82, and a noise component NB based on the corrected pixel vector _k Separating means 85 for separating and outputting the data.
[0113]
The pixel vector calculation means 82 calculates a pixel vector as follows. FIG. 25 is a diagram for explaining a pixel vector calculation method. Note that this pixel vector is calculated using the band-limited image signal B in the entire frequency band. _k This is performed for all the pixels in the image represented by (wavelet transform coefficient signal). When a certain pixel is a pixel of interest (shown in black in FIG. 25), a 7 × 7 region centered on the pixel of interest is set. Then, for 48 pixels in the vicinity of the target pixel in this region, pixel values of a certain length in 16 directions from 0 to 15 centering on the target pixel (in FIG. 25, for three pixels, for example, hatched pixels in the two directions). Is calculated, and the direction in which the difference between the average value and the pixel value of the target pixel is the smallest is determined. Note that, as shown in FIG. 26, the difference between the target pixel and the adjacent pixels in the eight directions may be obtained using eight pixels in the vicinity of the target pixel, and the direction with the smallest difference may be determined. The direction obtained in this way is the direction where the gradient of the density is the smallest, and faces the equal signal line, that is, the normal direction of the signal gradient. Then, a vector having the magnitude of the reciprocal of this direction and the difference obtained as described above is obtained as a pixel vector. Therefore, this pixel vector becomes larger as the density difference is smaller in the direction of the equal signal line. Note that when the difference is 0, the size of the pixel vector becomes infinite, so it is preferable to set an upper limit value (for example, 255 for 8 bits) for the size of the pixel vector.
[0114]
The direction in which the difference between the above average value and the pixel value of the target pixel (or the difference between neighboring pixels and the target pixel, hereinafter simply referred to as “difference”) is the signal gradient direction, and the pixel vector is obtained in this direction. You can also. In this case, the above difference can be used as it is for the size of the pixel vector. In the present embodiment, the pixel vector is directed in the equal signal line direction, and the size of the pixel vector is described as the reciprocal of the difference.
[0115]
In the pixel vector correcting means 83, the pixel vector is corrected as follows. When the original image signal is wavelet transformed, detailed edge information is expressed in an image in a relatively high frequency band, edge information in the intermediate frequency band is displayed in an image in an intermediate frequency band, and low frequency is displayed in an image in a low frequency band. Edge information with a large bandwidth is expressed. Generally, the energy of an image in each frequency band is smaller as the frequency is higher, but the noise energy does not depend on the frequency band, so the image in the lower frequency band has better S / N. It becomes. Here, the portion in which noise is not mixed in the original image (see FIG. 27A) has a signal only in the edge portion in any band limited image (FIGS. 27B to 27D). If the variance of the pixel values in the predetermined area including the pixel for which the pixel vector is obtained is small in the relatively high-band restricted image, the pixel vector can be selected without referring to the pixel vector of the low-frequency band image. The obtained target pixel can be regarded as being in a flat portion.
[0116]
On the other hand, in the high-band limited image, the portion of the original image where noise is mixed (FIG. 28 (a)) is disturbed in the direction of the pixel vector due to the influence of noise, and the variance value becomes large (FIG. 28 (b)). The lower the frequency band, the smaller the influence of noise on the signal and the smaller the dispersion value (FIGS. 28C and 28D). Therefore, when the variance of pixel values in a predetermined region including the target pixel for which the pixel vector is obtained in the high-band restricted image is large, the pixel vector is obtained unless the corresponding pixel vector in the low-frequency band image is referred to. It is not known whether the target pixel is in a flat part or an edge part.
[0117]
For this reason, the pixel vector correcting unit 83 obtains a variance value of pixel values in, for example, a 3 × 3 region centered on the pixel of interest for which the pixel vector is obtained, for other pixels in the image in which the variance value is the same frequency band. If the area is relatively small compared to the above area, it is regarded as a flat part and the pixel vector is not corrected, and the pixel vector obtained by the pixel vector calculating means 82 is used as the corrected pixel vector as it is. On the other hand, if the variance value is relatively large compared to other regions in the image of the same frequency band, it is not known whether it is a flat part or an edge part, so that the corresponding value in the image of the low frequency band corresponds. By using the pixel vector of the pixel as the corrected pixel vector of the target pixel, the accuracy of whether the target pixel for which the pixel vector was obtained is in a flat portion or an edge portion is improved. As a result, the pixel vector of the flat portion is corrected to represent the flat portion, and the pixel vector of the edge portion is corrected to represent the edge portion.
[0118]
Note that the pixel vector correcting unit 83 may obtain the difference value between the target pixel and the neighboring pixels when the pixel vector is calculated as a variance value. As the difference value, for example, when a pixel vector is obtained from eight pixels near the target pixel, the difference value may be the sum of the differences between the target pixel and the eight neighboring pixels, or an average value of the differences.
[0119]
In the separating means 85, noise components are separated as follows. Pixels having a small corrected pixel vector obtained by the pixel vector correcting means 83 are assumed to be pixels that are in a flat part and include noise components, so that each pixel of the image represented by the smoothed signal in each frequency band is obtained. It is determined whether labeling is performed, that is, whether the determination target pixel is a pixel representing a noise component.
[0120]
When the pixel vector is small, the target pixel can be regarded as being in a flat portion, that is, a noise component, but may be in a minute edge portion. Therefore, in the separation unit 85, when the pixel vector is small, the pixel vector of the target pixel and the direction of the pixel vector of the neighboring pixel are referred to, and the neighboring pixel vector is focused as shown in FIG. If the pixel vector is oriented in the same direction as the pixel vector of the pixel, the pixel of interest is considered to be in the edge component, and the neighboring pixel vector is oriented in a different direction from the pixel vector of the pixel of interest as shown in FIG. If it is, it is preferable to regard the target pixel as being in the noise component. In FIG. 29, the number of each pixel indicates the direction of the pixel vector (see FIG. 26).
[0121]
Based on the result of the labeling, a smoothing process for reducing the pixel value is performed for the pixels considered to be in the noise component. This process is a process for changing the pixel itself, that is, a process for changing the local contrast in each band-limited image, based on the information (labeling result) regarding the noise component obtained as described above. Only the band limited image signals HHk, HLk, and LHk representing the contrast of the image in each frequency band are processed, and the processed band limited image signals HHk ′, HLk ′, LHk ′ (k = 1 to n; hereinafter B) _k '). Then, from each band limited image signal HHk, HLk, LHk, the corresponding processed band limited image signal B _k By subtracting ′, noise band limited image signals NHHk, NHLk, NLHk (ie, NB) as noise components _k = B _k -B _k ′) Is separated.
[0122]
Thereafter, the obtained noise band limited image signal NB is obtained as in the second embodiment. _k On the other hand, the inverse wavelet transform means 64 repeatedly performs the inverse wavelet transform up to the maximum frequency band to obtain a noise signal S1, and based on the obtained noise signal S1, the arithmetic unit 65 performs the first implementation. Similar to the embodiment, the calculation shown in the equation (8) is performed to obtain the processed image signal Sproc.
[0123]
FIG. 30 is a flowchart showing the operation of the third embodiment. First, an original image signal Sorg is input to the image processing device 51 from a reading device or the like (step S21), and this original image signal Sorg is subjected to wavelet transform in the wavelet transform means 52, and wavelet transform coefficient signals for each frequency band are obtained. Is obtained (step S22). Next, a pixel vector is calculated in the pixel vector calculation means 82 based on each wavelet transform coefficient signal as described above (step 23). After the calculation of the pixel vector, the pixel vector correcting unit 83 corrects the pixel vector to obtain a corrected pixel vector (step S24).
[0124]
Next, the separation unit 85 performs a process of separating the noise component based on the corrected pixel vector obtained by the pixel vector correcting unit 83, and the noise band limited image signal NB. _k Are separated (step S25). Thereafter, the noise band limited image signal NB is processed by the inverse wavelet transform means 64. _k Inverse wavelet transform is repeatedly performed up to the maximum frequency band to obtain a noise signal S1 (step S26). Then, the processed image signal Sproc is obtained by performing the calculation shown in the above equation (8) using the noise signal S1 (step S27), and an image based on the processed image signal Sproc is displayed on a monitor (not shown) (step S28). . The operator observes the displayed image, and if it is necessary to change the degree of noise removal (step S29), the operator inputs the degree of change to the processing means 73. As a result, the processing means 73 changes the enhancement coefficient α (Sorg) in the above equation (8), returns to step S27, and performs the processing from step S28 to step S29. If the degree of noise removal is appropriate, step S29 is affirmed and the process is terminated.
[0125]
As described above, also in the third embodiment, the level of the noise signal S1 subtracted from the original image signal Sorg can be arbitrarily changed by changing the value of the enhancement coefficient α (Sorg) in the above equation (8). Thus, the degree of noise removal from the original image signal Sorg can be arbitrarily changed.
[0126]
In addition, since the processed signals HHk ′, HLk ′, LHk ′, and LLn ′ obtained in each frequency band are subjected to processing for reducing noise components, the processed image signal Sproc that is finally obtained is also The noise component is reduced. Therefore, it is possible to obtain a processed image signal Sproc that can reproduce a high-quality image in which noise is not noticeable.
[0127]
Next, a fourth embodiment of the present invention will be described. FIG. 31 is a schematic block diagram showing the configuration of the image processing apparatus according to the fourth embodiment of the present invention.
[0128]
FIG. 31 corresponds to FIG. 24 showing the configuration of the noise separating means 72 in the third embodiment, and differs in that a smoothing means 84 is provided in place of the separating means 85. The wavelet transform unit 61, the pixel vector calculation unit 82, the pixel vector correction unit 83, and the inverse wavelet transform unit 64 are the same as those in the third embodiment, and thus detailed description thereof is omitted here.
[0129]
The smoothing unit 84 performs a smoothing process on the wavelet transform coefficient signal based on the corrected pixel vector corrected by the pixel vector correcting unit 83 as follows to obtain a smoothed signal. The smoothing process is performed on the band limited image signals HHk, HLk, and LHk in each frequency band. FIG. 32 is a diagram for explaining the smoothing process in the smoothing means 84. When the pixel value of each pixel has the value shown in FIG. 32A in the 3 × 3 region centered on the target pixel, the pixel vector (corrected pixel vector) is as shown in FIG. Then, filtering is performed by a smoothing filter using a pixel of interest, a pixel in the pixel vector direction, and a pixel in the opposite direction to the pixel vector direction as indicated by diagonal lines in FIG. Here, as the smoothing filter, any filter having directionality may be used. For example, the average value filter shown in FIG. 33A or the smoothing shown in FIG. A filter can be used. When the average value filter shown in FIG. 33A is used, the pixel value shown in FIG. 32A is smoothed as shown in FIG. When the smoothing filter shown in FIG. 33B is used, the pixel value of the target pixel is smoothed to 141 as shown in FIG. By performing smoothing in this way, for example, when noise is mixed on the edge, the noise can be made inconspicuous. Further, by performing smoothing in the flat portion even if not on the edge, the noise in the flat portion can be made inconspicuous. Note that the smoothed band-limited image signal (wavelet transform coefficient signal) is a smoothed signal (smoothed band-limited image signal).
[0130]
Here, smoothing is performed using pixels in the pixel vector direction and pixels in the direction opposite to the pixel vector direction, but smoothing may be performed using only pixels in the pixel vector direction. In this case, the target pixel shown in FIG. 32A is smoothed so as to have a value of 99 (= (101 + 98) / 2).
[0131]
Further, when the pixel vector is obtained from the 48 pixels in the vicinity of the target pixel and the direction of the pixel vector is the direction shown in FIG. 35, the target pixel and the pixel in the pixel vector direction are indicated by the hatched lines in FIG. (Furthermore, smoothing may be performed using a pixel in a direction opposite to the pixel vector). Specifically, an average value of pixel values of all seven pixels indicated by hatching shown in FIG. 35 may be used as the pixel value of the target pixel.
[0132]
When the smoothed signals of the respective frequency bands are obtained in this way, the noise band limited image signals NHHk, NHLk,... Are subtracted from the corresponding band limited image signals HHk, HLk, LHk before smoothing. NLHk (ie NB _k = B _k -B _k ′) Is separated.
[0133]
Thereafter, the obtained noise band limited image signal NB is obtained as in the third embodiment. _k On the other hand, the inverse wavelet transform means 64 repeatedly performs the inverse wavelet transform up to the maximum frequency band to obtain a noise signal S1, and based on the obtained noise signal S1, the arithmetic unit 65 performs the first implementation. Similar to the embodiment, the calculation shown in the equation (8) is performed to obtain the processed image signal Sproc.
[0134]
FIG. 36 is a flowchart showing the operation of the fourth embodiment. As shown in FIG. 36, first, wavelet transform is performed on the original image signal Sorg by the wavelet transform means 61 to obtain a wavelet transform coefficient signal for each frequency band (step S31). Next, a pixel vector is calculated in the pixel vector calculation means 82 based on each wavelet transform coefficient signal as described above (step S32). After the calculation of the pixel vector, the pixel vector correction unit 83 corrects the pixel vector to obtain a corrected pixel vector (step S33). Then, based on the corrected pixel vector, each wavelet transform coefficient signal is subjected to smoothing processing in the smoothing means 84 to obtain a smoothed signal (step S34), and then noise is generated based on the smoothed signal. The noise band limited image signal NB is processed by separating the components. _k Are separated (step S35). Thereafter, the processes of steps S27 to S29 shown in the flowchart (FIG. 30) showing the operation of the third embodiment are performed.
[0135]
Here, when noise is mixed in the original image, the edge component in the image also includes noise. In this case, if the noise component is separated based on the pixel vector and the edge component is enhanced, the noise included in the edge component is also enhanced. In the present embodiment, since the smoothing unit 84 performs the smoothing process based on the direction of the pixel vector or the corrected pixel vector, the noise component on the edge can be extracted without losing the edge component, and other than the edge. Since the noise on the flat portion can also be extracted, the noise on the edge becomes inconspicuous and the noise in the flat portion becomes inconspicuous, and a high-quality image can be reproduced.
[0136]
Note that after the noise component and the edge component of the smoothed band limited image signal are separated based on the size of the pixel vector, the smoothing process for the noise component and / or the edge component is performed on the smoothed band limited image signal. By performing enhancement processing to obtain a processed band limited image signal, and using the processed band limited image signal to obtain a noise signal included in the band limited image signal before smoothing, noise on the edge is reduced. Edge emphasis can be performed without making it conspicuous, and noise on the flat portion can be further reduced, so that a higher quality image can be reproduced.
[0137]
As mentioned above, although preferable embodiment of this invention was described, this invention is not limited to the said embodiment, As long as the summary of invention is not changed, it can change variously.
[0138]
For example, in each of the above embodiments, the enhancement coefficient to be multiplied by the noise signal S1 in the above equation (8) is a function of the original image signal Sorg, but is not limited to this and may be a constant.
[0139]
In the third and fourth embodiments, the pixel vector calculation means 82 has the smallest difference between the pixel value of the target pixel and the average value of the pixel values of the neighboring pixels (or the pixel value of the neighboring pixels). Although the direction is obtained as the direction of the pixel vector, the direction in which the difference is the second smallest may be obtained as the second pixel vector. Alternatively, when obtaining a pixel vector in the signal gradient direction, the direction in which the difference is the second largest may be obtained as the second pixel vector. Thus, by obtaining the second pixel vector, for example, when the edge component is bent as shown in FIG. 37A, two pixel vectors are obtained as shown in FIG. 37B. . Then, by performing smoothing using both the first and second pixel vectors in the smoothing means 84, the edge component can be smoothed more accurately while maintaining its directionality.
[0140]
In addition, a relatively large edge included in the image remains in the low frequency band image, but the noise becomes smaller as the low frequency band image. Therefore, by making the direction of the pixel vector in one pixel of the band-limited image in a certain frequency band the same as the direction of the pixel vector of the pixel corresponding to one pixel in the image in the low-frequency band, the pixel becomes an edge. In the case of the component, the pixel vector represents the edge component more. On the other hand, when the pixel is in the noise component, the finer noise is reduced in the low frequency band image, so the pixel vector is in a random direction and the size is further reduced, so the pixel vector is more It represents a flat portion, that is, a noise component. Therefore, in the third and fourth embodiments, the pixel vector correction unit 83 replaces the process based on the variance value with the direction of the pixel vector in a pixel in a certain frequency band, and further in the image in the lower frequency band. By correcting the pixel vector so as to be in the same direction as the pixel vector corresponding to the certain pixel, it is possible to improve the accuracy of whether the pixel is in the edge component or the noise component. In particular, in the third embodiment, the noise component and the edge component can be accurately separated in the separating means 85.
[0141]
Further, in the third and fourth embodiments, the pixel vector is corrected by the pixel vector correcting unit 83. However, the separating unit 85 uses the pixel vector calculated by the pixel vector calculating unit 82 as it is, and the noise is corrected by the noise. The smoothing means 84 may perform smoothing by separating the components.
[0142]
In addition, for example, a plurality of radiation images are obtained by irradiating a subject composed of a soft part and a bone part, such as a human breast, with different energy from each other, and reading the plurality of radiation images to each of the plurality of radiation images. A soft part image signal representing a soft part image in which mainly soft parts of a subject are recorded by performing energy subtraction processing based on the plurality of image signals or a bone part in which mainly bone parts of the subject are recorded There are cases where a bone part image signal representing an image is obtained, and the obtained soft part image or bone part image is used as an observation target. In this case, in order to reduce the noise component of the soft part image or the bone part image, a smoothing process is performed on the bone part image signal to obtain a first smoothed image signal, and the first smoothed image signal is obtained from the original image signal. A first process for obtaining a soft part image signal representing a soft part image by subtracting the softened image signal is performed, and a smoothing process is further performed on the soft part image signal to obtain a second smoothed image signal. The second smoothed image signal is subtracted from the second process to obtain the bone image signal from which noise has been removed, and the first and second processes are repeated to reduce the noise component. An energy subtraction image generation method has been proposed (for example, Japanese Patent Laid-Open No. 5-236351). Here, in such an energy subtraction image generation method, when obtaining a smoothed image, the processing according to the present invention may be performed. As described above, in the energy subtraction image generation method, by obtaining the smoothed image signal by performing the processing according to the present invention, only the noise component can be reduced and the edge component can be made conspicuous. A soft part image or a bone part image can be obtained.
[0143]
In the second to fourth embodiments, the signal obtained by subjecting the original image signal Sorg to the wavelet transform is subjected to the processing based on the pixel vector as described above. The same processing as described above can be performed not only on the conversion but also on the band limited image signal for each frequency band obtained by the multi-resolution conversion method of the original image signal Sorg such as a Laplacian pyramid.
[Brief description of the drawings]
FIG. 1 is a schematic block diagram showing the configuration of an image processing apparatus according to a first embodiment of the present invention.
FIG. 2 is a diagram showing a main scanning direction and a sub-scanning direction of an original image
FIG. 3 is a block diagram showing an overview of band-limited image signal creation processing.
FIG. 4 is a diagram schematically illustrating band-limited image signal creation processing.
FIG. 5 is a diagram illustrating an example of a filter used for filtering processing;
FIG. 6 is a diagram showing details of low-resolution image signal creation processing;
FIG. 7 is a diagram showing an example of a filter used for interpolation processing
FIG. 8 is a schematic block diagram showing the configuration of processing means together with band-limited image signal creation means
FIG. 9 is a schematic block diagram showing the configuration of noise separation means.
FIG. 10 is a conceptual diagram showing an iris filter.
FIG. 11 is a diagram showing a mask for calculating a gradient vector in an iris filter.
FIG. 12 is a diagram for explaining calculation of a spatial filter
FIG. 13 is a diagram schematically illustrating conversion processing.
FIG. 14 is a flowchart showing the operation of the first embodiment.
FIG. 15 is a schematic block diagram showing a configuration of an image processing apparatus according to a second embodiment of the present invention.
FIG. 16 is a schematic block diagram showing the configuration of wavelet transform means
FIG. 17 is a block diagram showing processing performed in a wavelet transform unit;
FIG. 18 is a diagram illustrating a wavelet transform coefficient signal for each of a plurality of frequency bands.
FIG. 19 is a schematic block diagram showing the configuration of processing means together with band-limited image signal creation means.
FIG. 20 is a diagram for explaining the inverse wavelet transform;
FIG. 21 is a block diagram showing processing performed in an inverse wavelet transform unit;
FIG. 22 is a flowchart showing the operation of the second embodiment.
FIG. 23 is a schematic block diagram showing the configuration of processing means used in an image processing apparatus according to a third embodiment of the present invention, together with band-limited image signal creation means.
FIG. 24 is a schematic block diagram showing the configuration of noise separation means used in the image processing apparatus according to the third embodiment of the present invention.
FIG. 25 is a diagram for explaining calculation of a pixel vector (part 1);
FIG. 26 is a diagram for explaining calculation of a pixel vector (part 2);
FIG. 27 is a diagram showing a wavelet transform coefficient signal (part 1);
FIG. 28 is a diagram showing a wavelet transform coefficient signal (part 2);
FIG. 29 is a diagram illustrating a pixel vector reference result in the separating unit.
FIG. 30 is a flowchart showing the operation of the third embodiment.
FIG. 31 is a schematic block diagram showing the configuration of an image processing apparatus according to a fourth embodiment of the present invention.
FIG. 32 is a diagram for explaining smoothing processing (part 1);
FIG. 33 is a diagram illustrating an example of a smoothing filter.
FIG. 34 shows a smoothed pixel value.
FIG. 35 is a diagram for explaining smoothing processing (part 2);
FIG. 36 is a flowchart showing the operation of the fourth embodiment.
FIG. 37 is a diagram for explaining another example of smoothing processing;
[Explanation of symbols]
1,51 Image processing apparatus
2 Band-limited image signal creation means
3,53,73 Processing means
10 Filtering processing means
11, 24 Interpolation processing means
12 Subtractor
22, 62, 72 Noise separation means
25 Adder
26, 65 computing unit
52 Wavelet transform means
61 Wavelet Transformer
64 Inverse wavelet transform means

Claims (27)

In an image processing method for performing image processing for reducing noise of the original image on an original image signal representing the original image,
Create a band limited image signal from the original image signal,
Calculating a pixel vector at each pixel of the band limited image represented by the band limited image signal;
Correcting a pixel vector in one pixel of a band-limited image in one frequency band based on a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band;
Based on the corrected pixel vector , a noise signal having the same number of pixels as the original image signal is obtained,
By storing the noise signal together with the original image signal in a predetermined storage means, after removing a noise component of the original image signal based on the noise signal, the original image signal stored in the storage means and An image processing method, wherein a noise component of the original image signal can be removed again by using the noise signal. In an image processing method for performing image processing for reducing noise of the original image on an original image signal representing the original image,
Create a band limited image signal from the original image signal,
Calculating a pixel vector at each pixel of the band limited image represented by the band limited image signal;
Calculating a variance value of a predetermined region including one pixel of a band-limited image in one frequency band;
Determining whether to modify the pixel vector of the one pixel based on the variance value;
When it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector of the one pixel is changed to a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. Correct based on
Based on the corrected pixel vector , a noise signal having the same number of pixels as the original image signal is obtained,
By storing the noise signal together with the original image signal in a predetermined storage means, after removing a noise component of the original image signal based on the noise signal, the original image signal stored in the storage means and An image processing method, wherein a noise component of the original image signal can be removed again by using the noise signal. In an image processing method for performing image processing for reducing noise of the original image on an original image signal representing the original image,
Create a band limited image signal from the original image signal,
Calculating a pixel vector at each pixel of the band limited image represented by the band limited image signal;
Smoothing the band limited image signal based on the pixel vector to obtain a smoothed band limited image signal;
Based on the smoothed band limited image signal , a noise signal having the same number of pixels as the original image signal is obtained,
By storing the noise signal together with the original image signal in a predetermined storage means, after removing a noise component of the original image signal based on the noise signal, the original image signal stored in the storage means and An image processing method, wherein a noise component of the original image signal can be removed again by using the noise signal. Correcting a pixel vector in one pixel of a band-limited image in one frequency band based on a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band ;
4. The image processing method according to claim 3, wherein the smoothed band limited image signal is obtained based on the corrected pixel vector instead of the pixel vector. Calculating a variance value of a predetermined region including one pixel of a band-limited image in one frequency band;
Determining whether to modify the pixel vector of the one pixel based on the variance value;
When it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector of the one pixel is changed to a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. Correct based on
4. The image processing method according to claim 3, wherein the smoothed band limited image signal is obtained based on the corrected pixel vector instead of the pixel vector. The image processing method according to any one of claims 1 5, characterized in that you create the band-limited image signal by the original image signal multiresolution transform a. The image processing method according to claim 6 , wherein the multi-resolution conversion is conversion by Laplacian pyramid decomposition or wavelet conversion. Wherein also based on the pixel vector in pixels near each pixel, the image processing method of any one of claims 1 7, characterized in that to obtain the noise signal. When the setting value of the parameter indicating the degree of removal of the noise component is changed, the original image signal and the noise signal are read from the storage unit, and the read original image signal and the noise signal are changed to the changed parameter. the image processing method according to any one of claims 1 to 8, characterized in that for removing noise components of the original image signal on the basis of. In an image processing apparatus that performs image processing for reducing noise of the original image on an original image signal representing the original image,
Band-limited image signal creating means for creating a band-limited image signal from the original image signal;
Pixel vector calculation means for calculating a pixel vector in each pixel of the band limited image represented by the band limited image signal;
Correcting means for correcting a pixel vector in one pixel of a band-limited image in one frequency band based on a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band;
Noise signal acquisition means for obtaining a noise signal having the same number of pixels as the original image signal based on the corrected pixel vector ;
First storage means for storing the original image signal;
Second storage means for storing the noise signal acquired by the noise signal acquisition means;
Means for removing a noise component of the original image signal based on the noise signal, and after removing the noise component of the original image signal based on the noise signal, stored in the first storage means; An image comprising noise removal means capable of re-removing a noise component of the original image signal using an original image signal and a noise signal stored in the second storage means Processing equipment. In an image processing apparatus that performs image processing for reducing noise of the original image on an original image signal representing the original image,
Band-limited image signal creating means for creating a band-limited image signal from the original image signal;
Pixel vector calculation means for calculating a pixel vector in each pixel of the band limited image represented by the band limited image signal;
A distributed value calculating means for calculating a variance value of a predetermined area including the one pixel of the band-limited image in one frequency band,
Determining means for determining whether to correct a pixel vector of the one pixel based on the variance value;
When it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector of the one pixel is changed to a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. Correction means for correcting based on;
Noise signal acquisition means for obtaining a noise signal having the same number of pixels as the original image signal based on the corrected pixel vector ;
First storage means for storing the original image signal;
Second storage means for storing the noise signal acquired by the noise signal acquisition means;
Means for removing a noise component of the original image signal based on the noise signal, and after removing the noise component of the original image signal based on the noise signal, stored in the first storage means; An image comprising noise removal means capable of re-removing a noise component of the original image signal using an original image signal and a noise signal stored in the second storage means Processing equipment. In an image processing apparatus that performs image processing for reducing noise of the original image on an original image signal representing the original image,
Band-limited image signal creating means for creating a band-limited image signal from the original image signal;
Pixel vector calculation means for calculating a pixel vector in each pixel of the band limited image represented by the band limited image signal;
Smoothing means for smoothing the band limited image signal based on the pixel vector to obtain a smoothed band limited image signal;
Noise signal acquisition means for obtaining a noise signal having the same number of pixels as the original image signal based on the smoothed band-limited image signal ;
First storage means for storing the original image signal;
Second storage means for storing the noise signal acquired by the noise signal acquisition means;
Means for removing a noise component of the original image signal based on the noise signal, and after removing the noise component of the original image signal based on the noise signal, stored in the first storage means; An image comprising noise removal means capable of re-removing a noise component of the original image signal using an original image signal and a noise signal stored in the second storage means Processing equipment. Correcting means for correcting a pixel vector in one pixel of a band-limited image in one frequency band based on a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band; In addition,
Said smoothing means, instead of the pixel vector, based on the pixel vector that has been modified by the modifying means, the image processing according to claim 12, characterized in that to obtain a smoothed band-limited image signal apparatus. Dispersion value calculating means for calculating a dispersion value of a predetermined region including one pixel of the band-limited image in one frequency band;
Determining means for determining whether to correct a pixel vector of the one pixel based on the variance value;
When it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector of the one pixel is changed to a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. And a correction means for correcting based on,
Said smoothing means, instead of the pixel vector, based on the pixel vector that has been modified by the modifying means, the image processing according to claim 12, characterized in that to obtain a smoothed band-limited image signal apparatus. The band-limited image signal forming means, the image processing of any one of claims 10 to 14, characterized in that the original image signal is to create the band-limited image signal by multiresolution transform apparatus. The image processing apparatus according to claim 15 , wherein the multi-resolution conversion is conversion by Laplacian pyramid decomposition or wavelet conversion. The image processing according to any one of claims 10 to 16, wherein the noise signal acquisition unit acquires the noise signal based on a pixel vector in a pixel in the vicinity of each pixel. apparatus. Parameter setting means for setting a parameter indicating a degree of removing the noise component to the noise removing means;
When the parameter setting value is changed by the parameter setting unit, the noise removing unit reads the original image signal from the first storage unit and reads the noise signal from the second storage unit and reads the noise signal. The image processing apparatus according to claim 10, wherein noise of the original image is removed based on the original image signal and the noise signal and the changed parameter. A computer-readable recording medium storing a program for causing a computer to execute an image processing method for performing image processing for reducing noise of the original image on an original image signal representing the original image, wherein the program is the computer The image processing method to be executed by
Creating a band limited image signal from the original image signal;
Calculating a pixel vector at each pixel of the band limited image represented by the band limited image signal;
Correcting a pixel vector in one pixel of a band-limited image in one frequency band based on a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band;
Obtaining a noise signal having the same number of pixels as the original image signal based on the corrected pixel vector ;
Including storing the original image signal and the noise signal in a predetermined storage means,
After removing the noise component of the original image signal based on the noise signal, the noise component of the original image signal is removed again using the original image signal and the noise signal stored in the storage means A computer-readable recording medium characterized in that A computer-readable recording medium storing a program for causing a computer to execute an image processing method for performing image processing for reducing noise of the original image on an original image signal representing the original image, wherein the program is the computer The image processing method to be executed by
Creating a band limited image signal from the original image signal;
Calculating a pixel vector at each pixel of the band limited image represented by the band limited image signal;
A procedure for calculating a variance value of a predetermined area including one pixel of a band-limited image in one frequency band;
Determining whether to correct a pixel vector of the one pixel based on the variance value;
When it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector of the one pixel is changed to a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. Based on the corrective steps,
Obtaining a noise signal having the same number of pixels as the original image signal based on the corrected pixel vector ;
Including storing the original image signal and the noise signal in a predetermined storage means,
After removing the noise component of the original image signal based on the noise signal, the noise component of the original image signal is removed again using the original image signal and the noise signal stored in the storage means A computer-readable recording medium characterized in that A computer-readable recording medium storing a program for causing a computer to execute an image processing method for performing image processing for reducing noise of the original image on an original image signal representing the original image, wherein the program is the computer The image processing method to be executed by
Creating a band limited image signal from the original image signal;
Calculating a pixel vector at each pixel of the band limited image represented by the band limited image signal;
Smoothing the band limited image signal based on the pixel vector to obtain a smoothed band limited image signal;
Obtaining a noise signal having the same number of pixels as the original image signal based on the smoothed band-limited image signal ;
Including storing the original image signal and the noise signal in a predetermined storage means,
After removing the noise component of the original image signal based on the noise signal, the noise component of the original image signal is removed again using the original image signal and the noise signal stored in the storage means A computer-readable recording medium characterized in that Correcting a pixel vector in one pixel of a band-limited image in one frequency band based on a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band; Have
Procedure for obtaining the smoothed band-limited image signal, the instead of the pixel vector, according to claim 21 computer-readable of, wherein the obtaining the smoothed band-limited image signal based on pixel vectors said modified Recording medium. A procedure for calculating a variance value of a predetermined area including one pixel of a band-limited image in one frequency band;
Determining whether to correct a pixel vector of the one pixel based on the variance value;
When it is determined that the pixel vector of the one pixel is to be corrected, the pixel vector of the one pixel is changed to a pixel vector of a pixel corresponding to the one pixel in an image in a frequency band lower than the one frequency band. And a procedure for correcting based on
Procedure for obtaining the smoothed band-limited image signal, the instead of the pixel vector, according to claim 21 computer-readable of, wherein the obtaining the smoothed band-limited image signal based on pixel vectors said modified Recording medium. To create the band-limited image signal may be any one of claims 19 to 23, characterized in that the original image signal to create said band-limited image signal is a procedure performed by multiresolution transform Computer-readable recording media. The computer-readable recording medium according to claim 24 , wherein the step of creating the band-limited image signal is a step of performing the multi-resolution conversion by conversion using Laplacian pyramid decomposition or wavelet conversion. Procedure for obtaining the noise band-limited image signal, the acquisition of noise signal, claim 19, wherein 25 of said is a procedure carried out also based on the pixel vector in the pixels near the pixel 1 A computer-readable recording medium according to Item . A step of setting a parameter indicating a degree of removing the noise component;
In the procedure for removing the noise component, when the set value of the parameter is changed, the original image signal and the noise signal are read from the predetermined storage unit, and the read original image signal and the noise signal are changed. 27. The computer-readable recording medium according to claim 19, wherein the noise component of the original image signal is removed based on a parameter.

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