JP3934353B2 - Image processing apparatus, recording medium, and program - Google Patents

Description

【００８６】
但し、ＡＢＳ｛｝は絶対値を求める関数、Ｐ(x,y)は処理前画像の画素(x,y)の階調値、ｍ,ｎは差分積算領域Ｅを決める定数、ａ,ｂは自己相関を比較するズラシ量、ｗｘ,ｗｙは１つの中心画素(xc,yc)に対して自己相関特性を調べる範囲Ｗを決める定数である。
【００８７】
(xc,yc)=(4,4)、m=n=1(差分積算領域：３×３)、wx=xy=2(a=b=-2〜+2)とした場合において、a=b=+2のときの自己相関データＳ(a,b)の算出形態を図１９に示す。
【００８８】
なお、ｍ,ｎ,ｗｘ,ｗｙは予め設定された固定値として処理するようにしてもよいし、入力部６からオペレータによって適宜に変更可能に構成してもよい。
【００８９】
ところで、上記の数１では、自己相関データを２次元的に求めているが、以下では簡単化のため、１次元的な自己相関データを用いてその抽出原理を説明する。
【００９０】
具体的には、２次元の処理前画像内で互いに直交する2つの画素列方向であるｘ方向、ｙ方向それぞれに沿った周期的な画像パターンの繰り返しの存在を調べて周期画像領域を抽出する場合を想定して説明する。具体的には、ｘ方向、ｙ方向それぞれに沿った１次元的な自己相関データＨ(a)、Ｖ(b)を以下の数２、数３によって求める。
【００９１】
【数２】

【００９２】
【数３】

【００９３】
(xc,yc)=(4,4)、m=1(差分積算領域：３×１)、wx=2(a=-2〜+2)とした場合において、a=+2のときのｘ方向に沿った自己相関データＨ(a)の算出形態を図２０に、また、(xc,yc)=(4,4)、n=1(差分積算領域：１×３)、wy=2(b=-2〜+2)とした場合において、b=+2のときのｙ方向に沿った自己相関データＶ(b)の算出形態を図２１にそれぞれ示す。
【００９４】
つぎに、ステップＳＰ２３において、上記で求めた自己相関データ(Ｓ(a,b)、または、Ｈ(a)、Ｖ(b))に基づき、画像中における周期的な画像パターンの繰り返しの有無を調べることにより、網点情報を決定する処理を行う。
【００９５】
網点画像において同程度の中間階調値を有する部分のように、周期的な画像パターンの繰り返しが存在する部分では、その画像パターンの周期ごとに自己相関が高くなり、上記数１、数２、数３で求まる自己相関データは規則的に小さくなる。従って、まず、（Ａ）自己相関データの極小値を検索し、（Ｂ）それら極小値が所定レベル以下で、かつ、（Ｃ）それら極小値が規則的に存在していることを調べる。なお、（Ｃ）の処理は、注目画素に対する対称性を考慮する処理であるともいえる。
【００９６】
以下では、自己相関データＨ(a)を用いてその抽出原理を説明する。
【００９７】
図２２は(xc,yc)=(7,3)、m=1、wx=5(a=-5〜+5)とした場合のＰ(x,y)、Ｈ(a)の一例を示すデータとそのＨ(a)をグラフ化した図である。すなわち、同図は注目画素(xc,yc)=(7,3)について、当該注目画素を含む注目領域と当該注目領域以外の周辺領域との相関特性を示すものである。なお、a=0は、同じ画素どうしの自己相関であるので、Ｈ(0)=０となり極小値になる。
【００９８】
自己相関データＨ(a)について、上記（Ａ）の処理は、〔(Ｈ(k-1)＞Ｈ(k))and(Ｈ(k)＜Ｈ(k+1))〕の条件を満たすkを＋側と−側とで求める。この条件を満たすkについてのＨ(k)の値が極小値となる。
【００９９】
上記（Ｂ）の処理は、上記（Ａ）の条件を満たすＨ(k)、すなわち極小値となるＨ(k)が所定のしきい値以下となるか否かで判定する。このしきい値は、予め入力部６等によって複数が設定されており、例えば図２２においては、”ＳＬ１=7.5”が設定されている。そして、極小値となるＨ(k)がしきい値ＳＬ１以下となっていれば、上記（Ｂ）の処理において、所定レベル以下であると判定される。
【０１００】
上記（Ｃ）の処理では、例えば、上記（Ａ）の条件を満たす＋側のkをkp、−側のkをkmとしたとき(ABS{kp＋km}≦１)を満たすか否かで規則性の有無を判定する。Ｈ(a)に極小値が存在しても、ある程度大きかったり(しきい値ＳＬ１を越えていたり)、それら極小値が不規則に存在しているような場合は周期性が有るとは言い難い。この処理においては、上記（Ｂ）によりレベル判定が行えるので、周期性の有無をより確実に判定できる。また、上記（Ｃ）を用いて極小値の規則性を判別することにより、周期性の有無をさらに確実に判定できる。
【０１０１】
従って、上記（Ａ）、（Ｂ）、（Ｃ）の条件を全て満たす場合、注目画素(xc,yc)の周囲の範囲Ｗ内の画像に周期的な画像パターンの繰り返しが存在することになる。これに対して、図２３に示すような自己相関データＨ(a)には周期性が無く、周期的な画像パターンの繰り返しが存在しないこととなる。なお、文字領域ＣＲにおいては、通常、図２３の状態になる。
【０１０２】
上記では１次元的な処理について説明したが、上記と同様の処理を２次元的に行うことが好ましい。具体的には、１次元的な自己相関データＨ（ａ）の代わりに２次元的な自己相関データＳ(a,b)を用いて同様の処理を行うことにより、距離ｄＸ，ｄＹを得ることができる。
【０１０３】
具体的には、図２４に示すように、上記条件（Ａ），（Ｂ），（Ｃ）を満たす極小値を有する画素のうち、注目画素からの距離が最も小さな画素ＥＭを求める。そして、その画素ＥＭの位置に応じた距離ｄＸ，ｄＹを求めればよい。この各距離ｄＸ，ｄＹは、周期値算出部３３によって求められる。また、周期方向算出部３２は、画像パターンの繰り返しの方向である周期方向θを(arctan(ｄＹ/ｄＸ))として算出することも可能である。なお、精度をそれほど必要としない場合などには、条件（Ｃ）を満足しないものをも画素ＥＭとして採用しても良い。
【０１０４】
以上のようにして、指定された複数の注目画素(xc,yc)のそれぞれについて、距離ｄＸ，ｄＹが周期性データとして算出される。なお、上記の画素ＥＭは、注目画素に対して４つの方向に存在し得るが、このステップＳＰ２３においては、このうち１つの方向の画素ＥＭに関する距離ｄＸ，ｄＹを求めるだけでも良い。具体的には、各注目画素について、第１象限から第４象限の４つの象限のうちの所定の１つの象限に存在する画素の中から、画素ＥＭを選択するようにすればよい。
【０１０５】
さらに、このようにして得られた複数の注目画素についての距離ｄＸ，ｄＹを用いて、各距離ｄＸ，ｄＹの平均値を求める。そして、この平均値が、網点画像領域ＳＲに関する距離ｄＸ，ｄＹに関する算出結果として得られる。
【０１０６】
ここで、（距離ｄＸ，距離ｄＹ）の組合せは、（網点線数，網点角度）の組合せとして表現されることも可能であり、これらの表現は互いに等価である。すなわち、距離ｄＸ，距離ｄＹを求めることは、網点情報を求めことと等価である。以上のようにして、網点情報決定処理が行われる。
【０１０７】
なお、４方向ベクトル算出部４０は、この距離ｄＸ，距離ｄＹの情報を用いることによって、４方向ベクトルＶ１〜Ｖ４を求めることができる。具体的には、たとえば、距離ｄＸ，ｄＹに基づいて１つのベクトル（たとえばＶ１）を求めるとともに、そのベクトル（Ｖ１）に対して９０度ずつずらしたベクトルを他の３つのベクトル（Ｖ２〜Ｖ４）として求めることができる。また、その他の動作は、上記実施形態と同様に行えばよい。
【０１０８】
＜その他＞
上記の階調値（ないしは画素値）の大小と画像の明暗（黒白）との関係は、階調値が大きいほど画像が白くなる（言い換えれば階調値が小さいほど画像が黒くなる）ものとして規定される場合と、階調値が大きいほど画像が黒くなる（言い換えれば階調値が小さいほど画像が白くなる）ものとして規定される場合とが存在する。本発明はいずれの場合にも適用することが可能である。
【０１０９】
また、上記実施形態においては、画像中に複数の種類の網点情報を有する網点画像領域ＳＲが混在する場合に、網点情報を各指定領域ＤＲごとに別個に求める技術について例示した。しかしながら、本発明はこれに限定されず、網点情報を所定のブロック単位で抽出することによっても、網点情報を的確に求めることが可能である。
【０１１０】
たとえば、図２５に示すように、所定の区分領域であるブロックＢｉ（図２５参照）ごとに網点情報を抽出することができる。図２５は、処理対象画像であるデジタル画像を複数のブロック（区分領域）Ｂｉ（ｉ＝１，...，Ｎ；ただしＮは区分数であり、ここではＮ＝２４）に区分した状態を表す図である。この図２５においては、各ブロックＢｉは破線で区分された各領域として示されている。
【０１１１】
ここでは、１つのブロックＢｉ内に最大１個の網点画像領域ＳＲが存在するように、処理対象画像が区分されているものとする。このように区分されたブロックＢｉごとに網点情報を抽出することにより、画像中に複数の種類の網点情報を有する網点画像領域ＳＲが混在する場合であっても、各網点画像領域ＳＲごとに異なる網点情報を的確に求めることができる。
【０１１２】
さらに、上記実施形態においては、コンピュータにおいてソフト的に上記各機能を実現することにより画像処理装置１を構成したが、これに限定されず、ロジック回路などのハードウエアを用いて同様の処理を実現する画像処理装置を構築するようにしても良い。
【０１１３】
【発明の効果】
以上のように、請求項１ないし請求項１１に記載の発明によれば、網点情報に基づいて所定の網点と４つの隣接網点のそれぞれとを結ぶ４つのベクトルを求め、注目画素からその４つのベクトルのそれぞれに相当するベクトル量だけ移動させた４つの近傍画素を選択し、その４つの近傍画素と注目画素とを用いて、注目画素が網点画像領域内の画素であるか文字領域内の画素であるかについての判定することができるので、デジタル画像内における網点画像領域と文字領域とを分離することが可能である。
そして、網点画像領域内の画素であると判定された画素についてはぼかし処理および再網点化処理を施した結果を採用し、文字領域内の画素であると判定された画素についてはぼかし処理および再網点化処理を施さない結果を採用するので、網点画像領域と文字領域とを分離してスクリーン線数変換処理を行うことが可能である。
【０１１４】
特に、請求項２に記載の発明によれば、デジタル画像に対する平滑化処理を行う手段をさらに備え、判定手段は、平滑化処理が施された画像に対して判定処理を行うので、ノイズを抑制することができる。
【０１１５】
また、請求項３に記載の発明によれば、判定手段は、注目画素およびその周辺画素を含む領域に対するメディアンフィルタ処理を用いて、当該判定処理における判定結果を修正するので、ノイズを抑制することができる。
【０１１６】
さらに、請求項４に記載の発明によれば、判定手段は、文字領域として判定される領域を拡張する太らせ処理を用いて判定結果を修正するので、文字領域と網点画像領域との境界領域において文字領域としての判定を優先することができる。したがって、たとえばスクリーン線数変更処理をさらに行う場合においては、文字を鮮明に維持した上で網点画像領域の線数を変換することが可能になる。
【０１１７】
また、請求項６に記載の発明によれば、網点情報抽出手段によって抽出された情報を網点情報として自動的に取得するので、操作性が高い。
【０１１８】
さらに、請求項７に記載の発明によれば、判定処理を行うべき領域を指定するので、処理対象領域を限定することによる高速化を図ることができる。
【０１１９】
さらに、請求項８に記載の発明によれば、網点情報取得手段は、領域指定手段により指定された領域ごとに網点情報を取得するので、的確に網点情報を取得することができる。
【０１２０】
また、請求項９に記載の発明によれば、網点情報抽出手段は、網点情報を所定のブロック単位で抽出するので、的確に網点情報を取得することができる。
【図面の簡単な説明】
【図１】本発明の実施形態に係る画像処理装置１のハードウエア構成を表す図である。
【図２】画像処理装置１の機能ブロック図である。
【図３】画像処理装置１におけるスクリーン線数変換処理の動作を示すフローチャートである。
【図４】網点画像領域ＳＲ等を表す図である。
【図５】４方向ベクトルの算出原理を説明する図である。
【図６】ステップＳＰ３０の詳細動作を表すフローチャートである。
【図７】ステップＳＰ４０の詳細動作を示すフローチャートである。
【図８】元の画像の網点画像領域ＳＲおよび文字領域ＣＲを示す図である。
【図９】元の画像の各領域ＳＲ，ＣＲの画素値を表す図である。
【図１０】加算結果ＲＶを示す図である。
【図１１】二値化処理の結果を示す図である。
【図１２】メディアンフィルタ処理結果の他の一例を示す図である。
【図１３】太らせ処理の結果を示す図である。
【図１４】平滑化フィルタの一例を示す図である。
【図１５】網点画像領域ＳＲに対する処理結果を示す概念図である。
【図１６】文字領域ＣＲに対する処理結果を示す概念図である。
【図１７】網点情報抽出処理の概要を表すフローチャートである。
【図１８】網点情報抽出処理の説明図である。
【図１９】中間値領域内の注目画素(xc,yc)の自己相関データを求める手法について説明する図である。
【図２０】ｘ方向に沿った自己相関データＨ(a)の算出形態を示す図である。
【図２１】ｙ方向に沿った自己相関データＶ(b)の算出形態を示す図である。
【図２２】Ｐ(x,y)、Ｈ(a)の一例を示すデータとそのＨ(a)をグラフ化した図である。
【図２３】周期的な画像パターンの繰り返しが存在しない自己相関データＨ(a)の一例を示す図である。
【図２４】周期方向θを示す図である。
【図２５】複数のブロックＢｉを示す図である。
【図２６】図２８の処理対象画像について生成されたマスク画像ＱＭの一例を表す図である。
【図２７】本発明を用いたスクリーン線数変換処理結果の一例を示す図である。
【図２８】処理対象画像の一例を示す図である。
【図２９】図２８の左上側の文字部分を拡大して示す図である。
【図３０】この処理対象画像に対して従来技術に係るスクリーン線数変換処理を施した画像を示す図である。
【符号の説明】
１ 画像処理装置
５０ 文字網点分離処理部
６０ スクリーン線数変換処理部
Ｂｉ ブロック
ＣＲ 文字領域
Ｄ０ 網点
Ｄ１〜Ｄ４ 各隣接網点
ＤＲ 指定領域
Ｅ１〜Ｅ４ 近傍画素
ＰＥ 注目画素
ＱＭ マスク画像
ＳＲ 網点画像領域
Ｖ１〜Ｖ４ ベクトル
Θ 網点角度[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing technique for processing a digital image, and more particularly to an image processing technique for processing a digital image having a halftone dot image area and a character area.
[0002]
[Related technologies]
There are cases where an image input using an image reading device such as a scanner is further output.
[0003]
In such a case, when the resolution of the input digital image is relatively large and the resolution of the output device is relatively small (for example, a halftone image having a line number of 133 lines or more is reduced to a smaller line number ( For example, in the case of outputting as an image having (106 lines), gradation collapse or tone jump may occur.
[0004]
In order to avoid such a situation, it is conceivable to perform a process of converting the number of halftone dots (hereinafter also referred to as “screen line number conversion process”) after the blurring process is performed on the input image. If only the process of converting the number of halftone dots is performed without performing the blurring process, the screen pattern having the old and new halftone line numbers causes a periodic interference state, and moire occurs. Therefore, in order to avoid this, it is necessary to perform a blurring process in the screen line number changing process.
[0005]
[Problems to be solved by the invention]
However, if the screen line number conversion process including the blurring process described above is performed on a digital image having both a halftone dot image area and a character area, characters in the character area are blurred. FIG. 28 is a diagram showing an example of a processing target image having a halftone dot image region SR and a character region CR, and FIG. 29 is an enlarged view of the upper left character portion of FIG. FIG. 30 is an image obtained by performing the above-described screen line number conversion processing on the processing target image. FIG. 30 shows a situation where characters are blurred as a result of the screen line number conversion process.
[0006]
This phenomenon is caused by performing the same processing on both areas (halftone image area and character area) having different properties. That is, it is required to separate the halftone dot image area and the character area in the digital image.
[0007]
Further, separating the halftone dot image area and the character area as described above is required not only when performing screen line number conversion processing, but also when performing various processes in digital image processing having both areas. Technology.
[0008]
In view of the above problems, an object of the present invention is to provide a technique capable of separating a halftone dot image area and a character area in a digital image.
[0009]
[Means for Solving the Problems]
  In order to achieve the above object, an image processing apparatus according to claim 1 is provided.Digital image acquisition means for acquiring a digital image; andHalftone dot informationAs the number of halftone lines and halftone angleHalftone dot information acquisition means for acquiringThe resolution of the digital image, the number of halftone lines, and the halftone angle;Based on the above, a means for obtaining four vectors connecting a predetermined halftone dot and each of the four adjacent halftone dots, and four neighboring pixels moved from the target pixel by a vector amount corresponding to each of the four vectors. Means for selecting, the pixel of interest and the four neighboring pixelsAn addition result obtained by adding the absolute values of the differences from each of the two is obtained, and when the addition result is smaller than a predetermined value, it is determined that the target pixel is in the halftone image region, and the addition result is the predetermined one. By determining that the pixel of interest is in the character area if greater than the valueA determination unit that performs a determination process as to whether the target pixel is a pixel in a halftone dot image region or a pixel in a character region;A mask image generating unit configured to generate a mask image that is a binary image in which pixels in the halftone dot image region and pixels in the character region have different gradation values based on the result of the determination process; A blur processing unit that performs processing, a re-halftone processing unit that performs re-halftone processing for re-dotting the image obtained by the blur processing, and the halftone image region using the mask image The result of performing the blurring process and the re-halftone processing is adopted for the pixel determined to be, and the blurring process and the re-dotting process are not performed for the pixel determined to be in the character area Combining means for generating a composite image by adopting the result;It is characterized by providing.
[0011]
  Claim2The image processing apparatus described in claim1The image processing apparatus described above further includes means for performing a smoothing process on the digital image, and the determination unit performs the determination process on the image that has been subjected to the smoothing process.
[0012]
  Claim3The image processing apparatus according to claim 1.OrClaim2In the image processing apparatus according to the item 1, the determination unit corrects a determination result in the determination process by using a median filter process for an area including the target pixel and its surrounding pixels.
[0013]
  Claim4The image processing apparatus according to claim 1 is a device according to any one of claims 1 to.3In the image processing apparatus according to any one of the above, the determination unit corrects the determination result by using a fattening process for expanding an area determined as a character area.
[0014]
  Claim5The image processing apparatus according to claim 1 is a device according to any one of claims 1 to.4In the image processing device according to any one of the above, the halftone dot information acquisition unit includes a halftone dot information input unit that inputs the halftone dot information, and the information input using the halftone dot information input unit is It is obtained as halftone dot information.
[0015]
  Claim66. The image processing apparatus according to claim 1, wherein the halftone dot information acquiring unit extracts halftone dot information from the digital image. And the information extracted by the halftone dot information extracting means is acquired as the halftone dot information.
[0016]
  Claim7The image processing apparatus according to claim 1 is a device according to any one of claims 1 to.6The image processing apparatus according to any one of the above, further comprising: an area designating unit that designates an area where the determination process is to be performed.
[0017]
  Claim8The image processing apparatus described in claim7In the image processing apparatus described in the item 1, the halftone dot information acquiring unit acquires the halftone dot information for each area designated by the area designation unit.
[0018]
  Claim9The image processing apparatus according to claim 1 is a device according to any one of claims 1 to.8In the image processing apparatus according to any one of the above, the halftone dot information extracting unit extracts the halftone dot information in predetermined block units.
[0020]
  Claim10The recording medium described inBy using the CPU and memory of the computer,A computer according to claim 1 to claim.9A computer-readable recording medium on which a program for causing the image processing apparatus to function as described above is recorded.
[0021]
  Claim11The program described inBy using the CPU and memory of the computer,A computer according to claim 1 to claim.9Or a program for causing the image processing apparatus to function as described above.
[0022]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[0023]
<A. Configuration>
FIG. 1 is a conceptual diagram illustrating a hardware configuration of an image processing apparatus 1 according to an embodiment of the present invention. As shown in FIG. 1, an image processing apparatus 1 includes a CPU 2, a storage unit 3 including a semiconductor memory and a hard disk, a media drive 4 for reading information from various recording media, a display unit 5 including a monitor, a keyboard, a mouse, and the like. And a computer system (hereinafter simply referred to as “computer”) including an input unit 6 including a communication unit 7 that performs communication with other devices. The CPU 2 is connected to the storage unit 3, the media drive 4, the display unit 5, the input unit 6, the communication unit 7 and the like via the bus line BL and the input / output interface IF. The media drive 4 reads information recorded therein from a portable recording medium 9 such as a CD-ROM, DVD (Digital Versatile Disk), or flexible disk.
[0024]
This computer reads various software programs (hereinafter also referred to simply as “programs”) recorded on the recording medium 9 and executes the programs using the CPU 2 or the like, thereby realizing various operations as described later. It functions as the image processing apparatus 1. The program having each function is not limited to the case where the program is supplied (or distributed) via the recording medium 9, and is connected to the computer via a network (communication line) such as a LAN or the Internet and the communication unit 7. May be supplied (or distributed).
[0025]
As described above, the image processing apparatus 1 is constructed by software in a computer.
[0026]
FIG. 2 is a functional block diagram of the image processing apparatus 1. As shown in FIG. 2, the image processing apparatus 1 performs image processing on a digital image input from an image reading apparatus 70 such as a scanner, and outputs an image as a processing result to an image output apparatus 80 such as a print output apparatus. Is output.
[0027]
The image processing apparatus 1 includes an area designating unit 12 for designating an area to be processed, a halftone dot information input unit 20 for inputting halftone dot information, and four points from predetermined halftone dots to adjacent halftone dots in four directions. A 4-direction vector calculation unit 40 for obtaining a vector, a character halftone dot separation processing unit 50, and a screen line number conversion processing unit 60 are provided. The character halftone dot separation processing unit 50 includes a smoothing processing unit 51, a difference value addition processing unit 52, a binarization processing unit 53, a median filter processing unit 54, and a fattening processing unit 55. The line number conversion processing unit 60 includes a blur processing unit 61 and a composition processing unit 62. Each of these units is constructed by software in a computer by executing a predetermined program.
[0028]
The image processing apparatus 1 can determine whether each pixel of interest is a pixel in the halftone dot image region or a pixel in the character region by using the character halftone dot separation processing unit 50 or the like. The screen line number conversion processing can be performed using the determination result using the screen line number conversion processing unit 60 or the like. Detailed operations and the like of each processing unit will be described later.
[0029]
<B. Operation>
<Overview of operation>
FIG. 3 is a flowchart showing the operation of the screen line number conversion process in the image processing apparatus 1. The screen line number conversion process will be described with reference to FIG.
[0030]
First, in step SP10, the image processing apparatus 1 acquires a digital image from the image reading apparatus 70 or the like. As shown in FIG. 4, the digital image includes a halftone dot image region SR and a character region CR. Moreover, this digital image is acquired as a binary image, for example. Here, since it is preferable to increase the number of gradations in the following processing, at this time, the gradation values 0 and 1 of each pixel of the binary image are converted into 0 and 255 in advance for convenience. This binary image is actually converted into a multi-value image having 256 levels of gradation values by each processing described later.
[0031]
Further, the operator (operator) of the image processing apparatus 1 may manually specify the area to be processed in steps SP30 and SP40 (described later) as necessary. In other words, the processing target area can be designated by trimming. By limiting the processing target area in this way, it is possible to increase the processing speed.
[0032]
Specifically, as shown in FIG. 4, in the digital image P displayed on the screen, an area DR including a halftone image area SR can be designated as a process target area. The designated area DR may include not only the halftone dot image area SR but also the character area CR, and may include a plurality of halftone dot image areas SR having the same halftone dot information. Even in such a case, the subsequent processing can be performed satisfactorily. However, when the plurality of halftone dot image areas SR have different halftone dot information, it is preferable to designate each halftone dot image area SR as a separate area.
[0033]
This designation process is performed using the area designation unit 12. More specifically, the designated area DR can be designated as a rectangular area having an arbitrary size by a mouse operation or the like.
[0034]
Next, in step SP20, halftone dot information about the halftone dot image region SR included in the processing target region is acquired. This halftone dot information is information including “the number of halftone lines” and “halftone angle” for the screen pattern used when the halftone image region SR is generated. The number of halftone lines is the number of screen lines per inch of the screen pattern, and the halftone angle is the angle formed by the screen pattern and the image.
[0035]
Here, it is assumed that the operator inputs the halftone dot information. Specifically, using the halftone dot information input unit 20 (FIG. 2), the operator includes halftone dot information (more specifically, the number of halftone lines and the halftone angle) regarding the halftone dot image region SR. Information). As a result, the image processing apparatus 1 acquires halftone dot information.
[0036]
Also, as shown in FIG. 4, when a plurality of areas are designated as the process target area DR, it is preferable to obtain halftone dot information for each process target area (designated area). Thereby, even when the halftone dot information such as the number of halftone lines is different for each designated area, the halftone dot information can be accurately extracted.
[0037]
Although the halftone dot information of the halftone dot image area SR can be automatically extracted based on the halftone dot image area SR by using the halftone dot information extraction unit 30, the operation and the like will be described later. To do.
[0038]
Further, in step SP20, processing for obtaining four different vectors connecting a predetermined halftone dot and an adjacent halftone dot is further performed based on the halftone dot information. This process is performed by the four-direction vector calculation unit 40.
[0039]
FIG. 5 is a diagram for explaining the principle. For example, as shown in FIG. 5, when the halftone dot angle Θ of the halftone image region SR is 45 degrees, the halftone dots D1 to D4 adjacent to the predetermined halftone dot D0 are separated from the predetermined halftone dot D0. It is located in four directions, lower right, lower left, upper left, and upper right. Further, when the number of halftone lines (number of lines per inch) of this halftone image region SR is 150 lines, and the resolution (number of pixels per inch) of the processing target image is 600 (dpi). The distance corresponding to 600/150 = 4 pixels corresponds to the distance D from the halftone dot D0 to the adjacent halftone dot. Accordingly, in this case, the horizontal distance dX from the halftone dot D0 to the adjacent halftone dot D1 is a value obtained by multiplying the value D (= 4) by cos Θ (cos 45 degrees) (that is, about 2.8), and is vertical. The directional distance dY is a value obtained by multiplying the value D (= 4) by sin Θ (sin 45 degrees) (that is, about 2.8). Accordingly, a vector V1 = (dX, dY) having the halftone dot D0 as the start point and the adjacent halftone dot D1 as the end point is obtained.
[0040]
Then, similar distances dX and dY are obtained for all of the other adjacent halftone dots D2 to D4 in the three directions. That is, a vector V2 having a halftone dot D0 as a start point and an adjacent halftone dot D2 as an end point is obtained, and a vector V3 having a halftone dot D0 as a start point and an adjacent halftone dot D3 as an end point, and a halftone dot D0 as a start point and A vector V4 whose end point is the adjacent halftone dot D4 is obtained. In this way, four different vectors V1 to V4 connecting the predetermined halftone dot D0 and each of the adjacent halftone dots D1 to D4 are obtained. These four vectors V1 to V4 are used in step SP30 described later.
[0041]
Further, in step SP30 (FIG. 3), a process for generating a mask image for separating the character region CR (FIG. 4) and the halftone dot image region SR for the designated region DR is performed, and then in step SP40, A screen line number conversion process is performed using the mask image. Hereinafter, the operations in step SP30 and step SP40 will be described in detail with reference to FIGS. FIG. 6 is a flowchart showing the detailed operation of step SP30, and FIG. 7 is a flowchart showing the detailed operation of step SP40.
[0042]
<Mask image generation operation>
Next, the mask image generation operation (step SP30) will be described.
[0043]
8 to 13 are diagrams for explaining this operation. FIG. 8 is an enlarged view in which a partial region of the processing target image is extracted. FIG. 8A shows a partial region in the halftone dot image region SR, and FIG. 8B shows the character region CR. The partial area | region is shown. Each square unit region in FIG. 8 represents one pixel, and pixels indicated by diagonal lines represent black pixels in the processing target image. FIG. 9 is a diagram showing the pixel values of each region. FIG. 9 (a) corresponds to FIG. 8 (a), and FIG. 9 (b) corresponds to FIG. 8 (b). is doing. Further, FIGS. 10 to 13 are diagrams showing the pixel values of the respective regions according to the progress of the following steps, and FIG. 10 (a) shows the processing results for FIG. 9 (a). (B), FIG. 11, and FIG. 13 show the processing results for FIG. 9 (b).
[0044]
Below, the process of each step is demonstrated, referring FIG.
[0045]
First, as shown in FIG. 6, in step SP31, the smoothing processing unit 51 performs smoothing processing on the digital image. This smoothing process can be realized by using, for example, a smoothing filter as shown in FIG. This smoothing filter is a filter having a size of 3 × 3 pixels. The weighting coefficient of the center is 4 and the weighting coefficients of the surrounding 8 pixels are 1 and 2, respectively. By using this smoothing filter, it is possible to calculate a weighted average value in which the pixel value of the target pixel (center pixel) is considered in consideration of the pixel values of surrounding pixels.
[0046]
Here, the process (smoothing process) of step SP31 is not necessarily performed, but the smoothing process of step SP31 is performed before each of the following steps SP33 to SP38 in order to suppress noise at the time of determination. It is preferable to carry out. In other words, it is preferable to perform determination processing such as the following steps SP33 to SP37 on the image that has been subjected to the smoothing processing.
[0047]
In addition, when the processing target image is a binary image, by performing this smoothing process, it is possible to simultaneously perform a process for converting the pixel value of each pixel into a multivalued image. For example, when this binary image is converted into a multi-value image having 256 levels of gradation values, as described above, the gradation values 0 and 1 of each pixel of the binary image are set for convenience. This smoothing process is performed after conversion into 0,255 in advance. Each pixel of the image after the smoothing process also has a gradation value other than 0, 255, and is subjected to a weighted average process that takes into account the pixel values of surrounding pixels and is then multivalued. The
[0048]
Next, in steps SP33 to SP37 (FIG. 6), a determination process is performed as to whether the target pixel is a pixel in the halftone image area or a pixel in the character area. This determination process is roughly divided into the following five processes: (1) a process of selecting four neighboring pixels moved by a vector amount corresponding to each of the four vectors from the target pixel (step SP33); 2) a process for obtaining the absolute value of the difference between the target pixel and each of the four neighboring pixels (step SP34), and (3) a process for obtaining an addition result obtained by adding these four difference absolute values (step SP35). (4) A determination process based on the magnitude relationship between the addition result and a predetermined threshold (step SP36), and (5) a process of applying a median filter to the determination result (step SP37). Hereinafter, each process will be described.
[0049]
First, in step SP33, (1) a process of selecting four neighboring pixels moved from the target pixel by a vector amount corresponding to each of the four vectors is performed. FIG. 8A shows four neighboring pixels E1 to E4 that exist at positions shifted by vectors V1 to V4 with respect to the target pixel PE, respectively. Here, what was calculated | required in said step SP20 is used as vectors V1-V4. Therefore, when it is assumed that the target pixel PE exists at the position of the predetermined halftone dot D0, the four pixels existing at the positions of the adjacent halftone dots D1 to D4 are selected as the four neighboring pixels E1 to E4. It will be.
[0050]
Next, in step SP34, (2) a process for obtaining an absolute value of a difference between the target pixel PE and each of the four neighboring pixels E1 to E4 is performed. Specifically, the absolute value A1 of the difference between the pixel value EV of the target pixel PE and the pixel value EV1 of the neighboring pixel E1 is obtained, and the difference between the pixel value EV of the target pixel PE and the pixel value EV2 of the neighboring pixel E2 is obtained. Find the absolute value of. Similarly, the absolute value of the difference between the pixel value EV of the target pixel PE and the pixel value EV3 of the neighboring pixel E3 and the absolute value of the difference between the pixel value EV of the target pixel PE and the pixel value EV4 of the neighboring pixel E4 are obtained.
[0051]
In step SP35, the four difference absolute values obtained in step SP34 are added, and the addition result RV is obtained. Here, a value obtained by dividing the addition result by 4 (that is, an average value) is obtained as a new addition result RV, and the new addition result RV is compared with the threshold value TH. FIG. 10 is a diagram showing the addition result RV. FIG. 10 shows the processing result for FIG. 9 and illustrates the case where step SP31 is not performed for the sake of simplicity. As described above, FIG. 10A shows the processing result for FIG. 9A (that is, the processing result for the halftone image region SR), and FIG. 10B shows the processing result for FIG. 9B. A processing result (that is, a processing result for the character region CR) is shown. The processes in steps SP33 to SP35 are performed by the difference value addition processing unit 52.
[0052]
In the next step SP36, the addition result RV is compared with a predetermined threshold value TH, and binarization processing is performed based on the magnitude relationship. This binarization processing is performed by the binarization processing unit 53. Specifically, when the addition result RV is equal to or greater than the threshold value TH, the pixel value of the target pixel PE is “1”, and when the addition result RV is smaller than the threshold value TH, the pixel value of the target pixel PE is “0”. By doing so, the pixel value of the target pixel can be binarized. FIG. 11 is a diagram showing the processing result of step SP36, and shows the processing result for FIG. 9B. Assuming that the threshold value TH is set to 130 as shown in FIG. 11, only the pixel value of the pixel having “255” in FIG. 10 is converted to “1”, and the pixel values of the other pixels are “0”. Is converted to.
[0053]
The predetermined threshold TH has a different value depending on the type of halftoning method (for example, error diffusion method or simple binarization method) when the halftone image region SR is formed. It may be determined. Specifically, when the type of the halftone method is known, the threshold TH corresponding to the type can be set. For example, the threshold TH for the halftone dot image region SR by the error diffusion method can be set to 80, and the threshold TH for the halftone dot image region SR by the simple binarization method can be set to 100. In this way, by setting the threshold value TH as a value corresponding to the type of the halftone method, this binarization process can be performed more appropriately. In addition, as information regarding the type of halftone dot conversion method, information input by the operator using the halftone dot information input unit 20 can be used as halftone dot information.
[0054]
Here, a value obtained by dividing the addition result by 4 (that is, an average value) is obtained as a new addition result RV, and the new addition result RV is compared with the threshold value TH. The addition result RV may be directly compared with the threshold value TH. The threshold value TH in that case may be determined as having a value four times the threshold value TH when the averaged addition result is used.
[0055]
Thus, the processing target image is binarized based on the magnitude relationship between the addition result RV and the threshold value TH. As a result of the binarization processing, a portion having the value “0” is determined as having a high possibility of being a pixel in the halftone image region, and a portion having the value “1” is determined to be a pixel in the character region. It is determined that there is a high possibility of being.
[0056]
This determination operation is based on the following principle.
[0057]
As shown in the upper part of FIG. 15, in the halftone image region SR, when the halftone dots expressing the same intermediate gradation value are continuous, the target pixel PE and its four neighboring pixels E1 to E4 are mutually connected. Have approximately equal pixel values. Therefore, as shown in the lower part of FIG. 15, the difference absolute value becomes small (ideally the difference absolute value = 0), and the addition result RV becomes a small value. Therefore, the portion having the value “0” is highly likely to be a pixel in the halftone image area. The lower part of FIG. 15 shows a state corresponding to the result of FIG.
[0058]
On the other hand, as shown in FIG. 16, in the character region CR, since the distribution of pixel values is non-uniform, the pixel value of the pixel of interest PE and its four neighboring pixels E1 to E4 often differ greatly. The difference absolute value becomes large and the addition result RV becomes a large value. Therefore, the portion having the value “1” is highly likely to be a pixel in the character area. 16 shows a state corresponding to the result of FIG. 10B. In FIG. 16, a white square area indicates an area having a pixel value between the black area and the white area.
[0059]
In the lower part of FIG. 16 and FIG. 10B, the processing of steps SP33 to SP36 is performed when a thin line having a width of about one pixel to several pixels (two pixels in FIG. 10B) continues in one direction. The applied results are shown. As shown in these figures, the absolute difference value remains as a relatively large value.
[0060]
Based on this principle, a pixel having the value “0” is determined to be a pixel in the halftone image area, and a portion having the value “1” is determined to be a pixel in the character area. Can do.
[0061]
Here, since the absolute difference values with respect to all neighboring pixels in the four directions toward the adjacent halftone dot are taken into account, erroneous determination due to direction dependency can be prevented. In other words, an accurate determination operation can be realized as compared with a case where only two of the four directions are considered.
[0062]
Further, in step SP37, a median filter process is performed on the determination result. This median filter is a filter having a predetermined size (for example, 3 × 3 pixels), and pays attention to the central value of a predetermined number (here, nine) of pixels including the target pixel and its surrounding pixels. It is a filter which makes a pixel value of a pixel. For example, when a median filter having a 3 × 3 pixel size is used, the fifth value among the nine pixels including the target pixel and its surrounding pixels can be set as the pixel value of the target pixel. When this median filter is applied, a pixel having a pixel value equal to or higher than the threshold TH is present alone without having a pixel with a high gradation value in the periphery (in short, it is present as an “enclave”) ), The pixel value of the pixel can be corrected (more specifically, corrected to zero). In this way, by correcting the determination result using the median filter process, it is possible to suppress noise due to the presence of the “outskirt”. When this median filter is applied to the processing result of FIG. 11, the result is the same as FIG.
[0063]
FIG. 12 is a diagram showing another example of the median filter processing in step SP37, FIG. 12A shows the processing results up to step SP36, and FIG. ) Shows the result of median filter processing. Referring to FIG. 12 (b), the “split” that existed in FIG. 12 (a) (in this case, it means a pixel having no more than three pixels of the same pixel value around it). ) Disappears. As described above, by performing the binarization process of step SP36 after the process of step SP37, it is possible to eliminate the enclave as described above. This median filter processing is performed by the median filter processing unit 54.
[0064]
In the next step SP38, a thickening process (specifically, a process of enlarging a portion determined as a character area) is performed on the binary image that is the determination result. For example, a maximum value selection filter having a size of 8 × 8 pixels, 16 × 16 pixels, or the like can be used. This maximum value selection filter is a filter that uses the maximum value of the pixel values in a predetermined area including the target pixel as the pixel value of the target pixel. As a result, the region of the pixel value “1” can be enlarged (in short, thickened). FIG. 13 shows the result of applying the fattening process of step SP38 to the result of FIG. 11 (or FIG. 12B) using a maximum value selection filter having a size of 8 × 8 pixels. . As shown in FIG. 13, the pixel value is “1” not only for the area corresponding to the thin line surrounded by the central thick line but also for the pixels in the peripheral area. That is, the determination result is corrected so that the area determined as the character area CR is enlarged. As described above, when the thickening process in step SP38 is used, the character area CR and the halftone dot image area SR are separated from each other in the boundary area (that is, the boundary area between the character area CR and the halftone dot image area SR). The determination as the region CR is prioritized. Accordingly, in the screen line number changing process described later, it is possible to convert the line number of the halftone dot image region SR while keeping the characters clear. This process is performed by the fattening processing unit 55.
[0065]
Through the processing as described above, an image that has been processed up to step SP38 is generated as a mask image. By using this mask image, it is possible to determine whether each pixel of interest is a pixel in the halftone dot image region or a pixel in the character region. That is, it is possible to separate the halftone dot image area and the character area in the digital image.
[0066]
Next, the screen line number conversion process in step SP40 will be described with reference to FIG. For example, a halftone dot image region SR having a halftone dot number of 150 lines can be reconverted into a halftone dot image having a different halftone dot number (here, 106 lines).
[0067]
Therefore, first, in step SP41, an averaging process (blurring process) is performed. Specifically, a blurring filter (averaging filter) that performs a simple averaging process with a size of 6 pixels × 6 pixels can be used.
[0068]
In the next step SP42, halftone processing (re-RIP) is performed again using a screen pattern having a different number of lines from the halftone image region SR. Thereby, the re-RIP image QA (see FIG. 2) can be obtained. The processing in steps SP41 and SP42 is performed by the blurring processing unit 61.
[0069]
Further, in step SP43, a synthesis process is performed. Specifically, the processing result image in step SP30 is used as the mask image QM (see FIG. 2). That is, for the pixel having a pixel value “0” in the mask image QM, the corresponding pixel of the re-RIP image QA generated in steps SP41 and SP42 is output, and for the pixel having a pixel value “1” in the mask image QM. The corresponding pixel of the original image QB (see FIG. 2) is output as it is.
[0070]
That is, the result of performing the processing of steps SP41 and SP42 is adopted for the pixel determined to be a pixel in the halftone image area, and the steps SP41 and SP42 are determined for the pixel determined to be a pixel in the character area. By adopting the result of not performing the above process, it is possible to separate the character area and the halftone image area in the designated area DR and perform the screen line number conversion process while suppressing image deterioration.
[0071]
Here, the case where the processing of steps SP41 and SP42 is performed for all the pixels in the designated region DR has been described, but the processing of steps SP41 and SP42 is performed only for the pixels whose pixel value of the mask image QM is “0”. You may make it synthesize | combine after performing. Since the process of steps SP41 and SP42 is not performed for the pixel having a pixel value “1” in the mask image QM, it can be processed efficiently. That is, the processing speed can be increased.
[0072]
FIG. 26 is a diagram illustrating an example of the mask image QM generated for the processing target image in FIG. In FIG. 26, the black area represents the pixel value “1”, and a state where the black area exists in the area corresponding to the character and its neighboring area is shown. In other words, it can be seen that the character region CR is accurately separated from the halftone image region SR.
[0073]
FIG. 27 is a diagram showing a result of the screen line number conversion processing in step SP40 using the mask image QM of FIG. 26, and is a partially enlarged view corresponding to FIG. In this way, it is possible to eliminate the blur of characters found in the prior art and output the characters clearly.
[0074]
<C. Modified example>
<Automatic extraction of halftone dot information>
In the above description, a case has been described in which halftone dot information of the halftone dot image region SR is obtained by an operator input. However, as described above, it is also possible to automatically extract halftone dot information of the halftone dot image region SR based on the halftone dot image region SR. Hereinafter, the automatic extraction operation will be described. This automatic extraction operation is performed by the halftone dot information extraction unit 30.
[0075]
In addition, when there are a plurality of areas designated using the area designating unit 12, it is preferable to obtain halftone dot information for each of the plurality of designated areas. Thereby, even when the halftone dot information of the halftone dot image areas SR included in each designated area is different from each other, the halftone dot information of each halftone dot image area SR can be accurately extracted.
[0076]
FIG. 17 is a flowchart showing an outline of halftone dot information extraction processing, and FIG. 18 is an explanatory diagram regarding halftone dot information extraction processing.
[0077]
As shown in FIG. 17, the intermediate value region extraction processing (step SP21), autocorrelation data calculation processing (step SP22), and halftone information determination processing (step SP23) are performed.
[0078]
First, in step SP21 of FIG. 17, intermediate value region extraction processing is performed.
[0079]
Specifically, this process is performed using a reduction average process for the designated region DR (or the entire processing target image). For example, as shown in FIG. 18A, by adding and averaging the pixel values of 64 pixels × 64 pixels in the designated area DR of the original image, the converted image (in other words, the reduced image) DS A pixel value of one pixel is once calculated. Then, as shown in FIG. 18B, a pixel ES whose pixel value is an intermediate value (for example, a gradation value of 10% to 90% of the maximum gradation value) among the pixels in the reduced image DS. A process of selecting and extracting each region ER corresponding to each selected pixel ES in the reduced image DS from the designated region DR of the original image is performed.
[0080]
Here, the pixel values after conversion of the black solid area and the blank part in the designated area DR become very large or very small values, while the character area and halftone image in the designated area DR. The pixel value of the converted pixel ES corresponding to the region portion is an intermediate value (intermediate value). Therefore, the halftone dot image region SR of the original image is expressed as a pixel having an intermediate value in the reduced image. Here, using this property, some pixels ES are extracted from the plurality of pixels having the intermediate value in the reduced image DS, and each area of the original image corresponding to each extracted pixel ES is determined as the intermediate value area. It is extracted as ER. That is, these intermediate value areas ER are extracted as candidates for the halftone image area SR.
[0081]
Next, in step SP22, an autocorrelation data calculation process described below is performed for each of the intermediate value regions ER.
[0082]
FIG. 19 is a diagram for explaining a method for obtaining autocorrelation data of a pixel of interest (xc, yc) in each intermediate value region ER. As the pixel of interest, for example, a pixel located at the center of the intermediate value region ER can be employed.
[0083]
The periodicity data for the target pixel (xc, yc) is calculated by examining the repetition of the periodic image pattern in the processing target image.
[0084]
Specifically, first, the autocorrelation data S (a, b) of the peripheral region of the target pixel (xc, yc) is obtained by the following equation (1).
[0085]
[Expression 1]

[0086]
Where ABS {} is a function for obtaining an absolute value, P (x, y) is a gradation value of the pixel (x, y) of the pre-processing image, m and n are constants for determining the difference integration region E, and a and b are The shift amount wx, wy for comparing the autocorrelation is a constant that determines the range W in which the autocorrelation characteristics are examined for one central pixel (xc, yc).
[0087]
When (xc, yc) = (4,4), m = n = 1 (difference integration region: 3 × 3), wx = xy = 2 (a = b = −2 to +2), a = FIG. 19 shows how the autocorrelation data S (a, b) is calculated when b = + 2.
[0088]
Note that m, n, wx, and wy may be processed as fixed values set in advance, or may be configured to be appropriately changed by the operator from the input unit 6.
[0089]
By the way, in the above equation 1, autocorrelation data is obtained two-dimensionally. However, for the sake of simplicity, the extraction principle will be described using one-dimensional autocorrelation data.
[0090]
Specifically, a periodic image region is extracted by examining the presence of repetition of a periodic image pattern along each of the x direction and the y direction, which are two pixel column directions orthogonal to each other in the two-dimensional pre-processing image. A case will be described. Specifically, one-dimensional autocorrelation data H (a) and V (b) along each of the x direction and the y direction are obtained by the following formulas 2 and 3.
[0091]
[Expression 2]

[0092]
[Equation 3]

[0093]
x when a = + 2 when (xc, yc) = (4,4), m = 1 (difference integration region: 3 × 1), and wx = 2 (a = −2 to +2) The calculation form of the autocorrelation data H (a) along the direction is shown in FIG. 20, and (xc, yc) = (4, 4), n = 1 (difference integration region: 1 × 3), wy = 2 ( FIG. 21 shows calculation forms of autocorrelation data V (b) along the y direction when b = + 2 when b = -2 to +2).
[0094]
Next, in step SP23, based on the autocorrelation data (S (a, b) or H (a), V (b)) obtained as described above, whether or not a periodic image pattern is repeated in the image is determined. By checking, halftone dot information is determined.
[0095]
In a halftone image, such as a portion having a similar halftone value, in a portion where the repetition of a periodic image pattern exists, the autocorrelation becomes higher for each cycle of the image pattern. , The autocorrelation data obtained by Equation 3 is regularly reduced. Therefore, first, (A) the minimum value of the autocorrelation data is searched, (B) these minimum values are below a predetermined level, and (C) whether these minimum values exist regularly is checked. Note that the process (C) can be said to be a process that takes into consideration symmetry with respect to the pixel of interest.
[0096]
Hereinafter, the extraction principle will be described using autocorrelation data H (a).
[0097]
FIG. 22 shows an example of P (x, y) and H (a) when (xc, yc) = (7,3), m = 1, wx = 5 (a = −5 to +5). It is the figure which plotted data and its H (a). That is, this figure shows the correlation characteristics between the attention area including the target pixel and the peripheral area other than the attention area for the target pixel (xc, yc) = (7, 3). Since a = 0 is an autocorrelation between the same pixels, H (0) = 0 and becomes a minimum value.
[0098]
For the autocorrelation data H (a), the process (A) satisfies the condition [(H (k-1)> H (k)) and (H (k) <H (k + 1))]. k is determined on the + side and the − side. The value of H (k) for k that satisfies this condition is the minimum value.
[0099]
The process (B) is determined based on whether or not H (k) that satisfies the condition (A), that is, H (k) that is the minimum value is equal to or less than a predetermined threshold value. A plurality of threshold values are set in advance by the input unit 6 or the like. For example, in FIG. 22, “SL1 = 7.5” is set. If H (k) that is the minimum value is equal to or less than the threshold value SL1, it is determined in the process (B) that the value is equal to or less than a predetermined level.
[0100]
In the processing of (C), for example, regularity is satisfied depending on whether or not (ABS {kp + km} ≦ 1) is satisfied when k on the + side satisfying the condition (A) is kp and k on the − side is km. The presence or absence of is determined. Even if there is a minimum value in H (a), it is difficult to say that there is periodicity if it is large to some extent (beyond the threshold SL1) or if these minimum values exist irregularly. . In this process, since the level can be determined by (B), the presence or absence of periodicity can be determined more reliably. Further, by determining the regularity of the minimum value using (C) above, the presence or absence of periodicity can be determined more reliably.
[0101]
Therefore, when all of the above conditions (A), (B), and (C) are satisfied, the image within the range W around the pixel of interest (xc, yc) has periodic image pattern repetition. . On the other hand, the autocorrelation data H (a) as shown in FIG. 23 has no periodicity and there is no periodic image pattern repetition. Note that the character region CR is normally in the state shown in FIG.
[0102]
Although one-dimensional processing has been described above, it is preferable to perform the same processing as described above two-dimensionally. Specifically, the distances dX and dY are obtained by performing the same processing using the two-dimensional autocorrelation data S (a, b) instead of the one-dimensional autocorrelation data H (a). Can do.
[0103]
Specifically, as shown in FIG. 24, the pixel EM having the smallest distance from the target pixel is obtained from the pixels having the minimum values that satisfy the above conditions (A), (B), and (C). Then, the distances dX and dY corresponding to the position of the pixel EM may be obtained. The distances dX and dY are obtained by the period value calculation unit 33. In addition, the periodic direction calculation unit 32 can also calculate the periodic direction θ, which is the image pattern repeating direction, as (arctan (dY / dX)). Note that, when accuracy is not so required, a pixel that does not satisfy the condition (C) may be used as the pixel EM.
[0104]
As described above, the distances dX and dY are calculated as the periodicity data for each of the plurality of designated target pixels (xc, yc). The pixel EM may exist in four directions with respect to the pixel of interest. However, in this step SP23, only the distances dX and dY related to the pixel EM in one direction may be obtained. Specifically, for each target pixel, the pixel EM may be selected from the pixels existing in a predetermined one quadrant among the four quadrants from the first quadrant to the fourth quadrant.
[0105]
Further, using the distances dX and dY for the plurality of target pixels obtained in this manner, an average value of the distances dX and dY is obtained. This average value is obtained as a calculation result regarding the distances dX and dY regarding the halftone image region SR.
[0106]
Here, the combination of (distance dX, distance dY) can also be expressed as a combination of (number of halftone lines, halftone angle), and these expressions are equivalent to each other. That is, obtaining the distance dX and the distance dY is equivalent to obtaining halftone dot information. As described above, the halftone dot information determination process is performed.
[0107]
The four-direction vector calculation unit 40 can obtain the four-direction vectors V1 to V4 by using information on the distance dX and the distance dY. Specifically, for example, one vector (for example, V1) is obtained based on the distances dX and dY, and the vector shifted by 90 degrees with respect to the vector (V1) is replaced with the other three vectors (V2 to V4). Can be obtained as Other operations may be performed in the same manner as in the above embodiment.
[0108]
<Others>
The relationship between the above-mentioned gradation value (or pixel value) and the brightness of the image (black and white) is that the image becomes whiter as the gradation value is larger (in other words, the image is blacker as the gradation value is smaller). There are cases where it is defined that the image is black as the gradation value is large (in other words, the image is white as the gradation value is small). The present invention can be applied to either case.
[0109]
Moreover, in the said embodiment, when the halftone dot image area | region SR which has several types of halftone dot information was mixed in the image, the technique which calculates | requires halftone dot information separately for every designated area | region DR was illustrated. However, the present invention is not limited to this, and halftone dot information can be obtained accurately by extracting halftone dot information in units of predetermined blocks.
[0110]
For example, as shown in FIG. 25, halftone dot information can be extracted for each block Bi (see FIG. 25) which is a predetermined segmented area. FIG. 25 shows a state in which a digital image, which is a processing target image, is divided into a plurality of blocks (partition areas) Bi (i = 1,..., N; where N is the number of sections, here N = 24). FIG. In FIG. 25, each block Bi is shown as each area divided by a broken line.
[0111]
Here, it is assumed that the processing target image is divided so that a maximum of one halftone dot image region SR exists in one block Bi. By extracting halftone dot information for each block Bi divided in this way, even if a halftone dot image area SR having a plurality of types of halftone dot information is mixed in the image, each halftone dot image area Different halftone dot information can be accurately obtained for each SR.
[0112]
Furthermore, in the above-described embodiment, the image processing apparatus 1 is configured by realizing the above functions in software on a computer. However, the present invention is not limited to this, and similar processing is realized using hardware such as a logic circuit. You may make it construct | assemble the image processing apparatus to perform.
[0113]
【The invention's effect】
  As described above, claims 1 to11According to the invention described in (4), four vectors connecting a predetermined halftone dot and each of the four adjacent halftone dots are obtained based on the halftone dot information, and only a vector amount corresponding to each of the four vectors is obtained from the target pixel. Selecting four moved neighboring pixels and using the four neighboring pixels and the target pixel to determine whether the target pixel is a pixel in the halftone image area or a character area Therefore, it is possible to separate the dot image area and the character area in the digital image.
  Then, the result of performing the blurring process and the re-halftone processing for the pixels determined to be the pixels in the halftone image area is adopted, and the blurring process is performed for the pixels determined to be the pixels in the character area. Since the result of not performing the re-halftone processing is adopted, it is possible to separate the halftone image area and the character area and perform the screen line number conversion process.
[0114]
  In particular, the claims2According to the above-described invention, the image processing apparatus further includes means for performing a smoothing process on the digital image, and the determination unit performs the determination process on the image that has been subjected to the smoothing process, so that noise can be suppressed.
[0115]
  Claims3According to the above-described invention, the determination unit corrects the determination result in the determination process using the median filter process for the region including the target pixel and the surrounding pixels, and thus noise can be suppressed.
[0116]
  And claims4According to the invention described in the above, since the determination unit corrects the determination result using a fattening process that expands the region determined as the character region, the determination unit determines the character region in the boundary region between the character region and the halftone image region. This determination can be prioritized. Therefore, for example, when the screen line number changing process is further performed, it is possible to convert the line number of the halftone image area while keeping the characters clear.
[0117]
  Claims6Since the information extracted by the halftone dot information extracting means is automatically acquired as halftone dot information, the operability is high.
[0118]
  And claims7According to the invention described in (1), since the area to be subjected to the determination process is designated, the speed can be increased by limiting the process target area.
[0119]
  And claims8Since the halftone dot information acquiring unit acquires halftone dot information for each region designated by the region designation unit, the halftone dot information can be accurately obtained.
[0120]
  Claims9Since the halftone dot information extracting unit extracts halftone dot information in a predetermined block unit, the halftone dot information can be accurately acquired.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a hardware configuration of an image processing apparatus 1 according to an embodiment of the present invention.
FIG. 2 is a functional block diagram of the image processing apparatus 1;
FIG. 3 is a flowchart showing an operation of a screen line number conversion process in the image processing apparatus 1;
FIG. 4 is a diagram illustrating a halftone dot image region SR and the like.
FIG. 5 is a diagram illustrating a calculation principle of a four-direction vector.
FIG. 6 is a flowchart showing the detailed operation of step SP30.
FIG. 7 is a flowchart showing a detailed operation of step SP40.
FIG. 8 is a diagram illustrating a halftone dot image region SR and a character region CR of an original image.
FIG. 9 is a diagram illustrating pixel values of regions SR and CR of an original image.
FIG. 10 is a diagram illustrating an addition result RV.
FIG. 11 is a diagram illustrating a result of binarization processing;
FIG. 12 is a diagram illustrating another example of the median filter processing result.
FIG. 13 is a diagram illustrating a result of a fattening process.
FIG. 14 is a diagram illustrating an example of a smoothing filter.
FIG. 15 is a conceptual diagram illustrating a processing result for a halftone dot image region SR.
FIG. 16 is a conceptual diagram showing a processing result for a character region CR.
FIG. 17 is a flowchart showing an outline of halftone dot information extraction processing.
FIG. 18 is an explanatory diagram of halftone dot information extraction processing.
FIG. 19 is a diagram illustrating a method for obtaining autocorrelation data of a pixel of interest (xc, yc) in an intermediate value region.
FIG. 20 is a diagram showing a calculation form of autocorrelation data H (a) along the x direction.
FIG. 21 is a diagram showing a calculation form of autocorrelation data V (b) along the y direction.
FIG. 22 is a graph of data showing an example of P (x, y) and H (a) and H (a).
FIG. 23 is a diagram illustrating an example of autocorrelation data H (a) in which there is no repetition of a periodic image pattern.
FIG. 24 is a diagram showing a periodic direction θ.
FIG. 25 is a diagram illustrating a plurality of blocks Bi.
FIG. 26 is a diagram illustrating an example of a mask image QM generated for the processing target image in FIG.
FIG. 27 is a diagram showing an example of a screen line number conversion process result using the present invention.
FIG. 28 is a diagram illustrating an example of a processing target image.
FIG. 29 is an enlarged view of the upper left character portion of FIG.
FIG. 30 is a diagram illustrating an image obtained by performing screen line number conversion processing according to a conventional technique on the processing target image;
[Explanation of symbols]
1 Image processing device
50 character halftone dot separation processor
60 Screen line number conversion processor
Bi block
CR character area
D0 halftone dot
D1 to D4 each adjacent halftone dot
DR specified area
E1-E4 neighboring pixels
PE pixel of interest
QM mask image
SR halftone image area
V1-V4 vector
Θ Halftone angle

Claims (11)

An image processing apparatus for processing a digital image,
Digital image acquisition means for acquiring a digital image;
Halftone dot information obtaining means for obtaining a halftone dot number and a halftone dot angle as halftone dot information of the digital image ;
Means for obtaining four vectors connecting a predetermined halftone dot and each of four adjacent halftone dots based on the resolution of the digital image, the number of halftone dots, and the halftone dot angle ;
Means for selecting four neighboring pixels moved from the pixel of interest by a vector amount corresponding to each of the four vectors;
An addition result obtained by adding the absolute values of the differences between the pixel of interest and each of the four neighboring pixels is obtained, and when the addition result is smaller than a predetermined value, the pixel of interest is determined to be in the halftone image area. Whether the pixel of interest is a pixel in the halftone image region or a pixel in the character region by determining that the pixel of interest is in the character region when the addition result is larger than a predetermined value . A determination means for performing a determination process on
Mask image generation means for generating a mask image which is a binary image in which the pixels of the halftone dot image region and the pixels of the character region have different gradation values based on the result of the determination process;
Blur processing means for performing blur processing on the digital image;
Re-halftone processing means for performing re-halftone processing for re-dotting the image obtained by the blurring processing;
For pixels that are determined to be in the halftone image area using the mask image, the result of performing the blurring process and the re-halftone processing is adopted, and for pixels that are determined to be in the character area Combining means for generating a combined image by adopting a result of not performing the blurring process and the re-halftone processing;
An image processing apparatus comprising: The image processing apparatus according to claim 1.
Means for smoothing the digital image;
Further comprising
The image processing apparatus , wherein the determination unit performs the determination process on the image that has been subjected to the smoothing process. The image processing apparatus according to claim 1 or 2,
The determination unit corrects a determination result in the determination process by using a median filter process for an area including the target pixel and its surrounding pixels . The image processing apparatus according to any one of claims 1 to 3,
The image processing apparatus characterized in that the determination means corrects the determination result by using a fattening process for expanding an area determined as a character area . The image processing apparatus according to any one of claims 1 to 4,
The halftone dot information acquisition means has halftone dot information input means for inputting the halftone dot information, and acquires information input using the halftone dot information input means as the halftone dot information. Image processing device. The image processing apparatus according to any one of claims 1 to 5,
The halftone dot information acquisition means includes halftone dot information extraction means for extracting the halftone dot information from the digital image , and acquires the information extracted by the halftone dot information extraction means as the halftone dot information. An image processing apparatus. The image processing apparatus according to any one of claims 1 to 6 ,
Area designating means for designating an area to be subjected to the determination process;
An image processing apparatus further comprising: The image processing apparatus according to claim 7 .
The image processing apparatus, wherein the halftone dot information acquiring unit acquires the halftone dot information for each region specified by the region specifying unit . Claims 1 image processing apparatus according to claim 8,
The halftone dot information extracting unit extracts the halftone dot information in predetermined block units . A computer-readable recording medium on which a program for causing the computer to function as the image processing apparatus according to any one of claims 1 to 9 by using a CPU and a memory included in the computer is recorded. The program for functioning the said computer as an image processing apparatus in any one of Claims 1 thru | or 9 by using CPU and memory which a computer has.

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