WO2011086594A1 - 画像処理装置、及びその方法 - Google Patents
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- the present invention relates to an image processing apparatus and method for reducing image noise.
- NON-LOCAL MEANS An algorithm called NON-LOCAL MEANS has been proposed as an image processing technology that reduces random noise. Use a weight corresponding to the distance between vectors between a vector in which pixel values in a block centered on the pixel to be processed are arranged and a vector in which pixel values in a block centered on the peripheral pixel are arranged, and the neighboring pixels There is a technique of performing weighted averaging of the pixel values and replacing the pixel to be processed (for example, Non-Patent Document 1).
- Patent Document 2 Project a vector in which pixel values in a block centered on a pixel to be processed are arranged and a vector in which pixel values in a block centered on surrounding pixels are arranged into a subspace composed of selected base vectors, The distance between vectors in the partial space is calculated.
- the set of basis vectors to be selected is one combination for the image. Therefore, the distance between two vectors that originally have different image features may not increase. At this time, unevenness occurs by adding the signals of the texture portion in the flat portion, and sharpness is lost by adding the signals of different textures in the texture portion.
- the set of basis vectors to be selected is one combination with respect to the image. Therefore, in the flat portion of the image, the pixel values of the pixels in the texture portion are added, resulting in unevenness. There is a problem that sharpness is lost in the texture portion.
- the present invention has been made to solve the above-described problems of the prior art, and an object thereof is to provide an image processing apparatus capable of sufficiently suppressing noise without losing the sharpness of an image. .
- an image processing apparatus includes a first vector having a pixel value of a pixel in a first region including a first pixel in an image as an element, and a plurality of bases
- a first calculation unit for obtaining a magnitude of correlation with the vector
- a selection unit for selecting a base vector from the plurality of base vectors according to the magnitude of the correlation
- a second pixel in the image A second region is selected, and a first projection vector obtained by projecting the first vector onto a partial space constituted by the selected basis vector and a pixel value of a pixel in the region for each second region are arranged.
- a second calculation unit that performs the distance A weighted average unit that gives a larger weight to the second pixel as it is smaller, performs a pixel value obtained by weighted averaging of the second pixel, and calculates an output pixel value of the first pixel according to the distance;
- noise can be removed without losing sharpness in the texture portion, and smoothing can be performed while suppressing occurrence of unevenness in the flat portion.
- FIG. 1 is a diagram illustrating a configuration of an image processing apparatus according to a first embodiment.
- FIG. 1 is a block diagram illustrating an image processing apparatus 100 according to the first embodiment.
- the image processing apparatus 100 includes an inner product calculation unit 101, a selection unit 102, a projection unit 103, a distance calculation unit 104, and a weighted average unit 105.
- the inner product calculation unit 101 includes a first vector in which pixel values of pixels in a first region including pixels to be processed (hereinafter referred to as first pixels) in the input image are arranged as elements,
- the inner product with a plurality of (for example, M) basis vectors stored in the inner product calculating unit 101 is calculated.
- a plurality of contributions indicating the magnitude of correlation between each base vector and the first vector are calculated by the square of the calculated inner product.
- an example is shown in which an inner product is used to evaluate the magnitude of the correlation between the first vector and the base vector.
- the invention is not limited to the inner product as long as the magnitude of the correlation between the vectors can be evaluated. Is possible. The larger the square of the inner product, the higher the correlation between the two vectors.
- FIG. 2 is a diagram illustrating a calculation example of basis vectors stored in advance.
- a principal component analysis of vectors in which pixel values of pixels included in each partial image are arranged is performed, and the obtained principal component vectors 303 are used as basis vector groups.
- Used as The image 301 may be an input image or a learning image other than that.
- the inner product calculation unit 101 calculates basis vectors.
- a basis vector group (1) an eigenvector obtained by canonical correlation analysis of an image extracted in advance from one or a plurality of images, (2) a basis of a two-dimensional discrete Fourier transform, ( 3) A base of a two-dimensional discrete wavelet transform or the like may be used.
- the selection unit 102 selects a plurality of (for example, L, L ⁇ M) basis vectors to be sent to the projection unit 103 from a plurality of basis vectors according to the size of the inner product calculated by the inner product calculation unit 101. Details of the selection method will be described later.
- the projection unit 103 obtains a first projection vector obtained by projecting the first vector into the partial space constituted by the basis vectors selected by the selection unit 102.
- the pixels of the second region including the second pixel not to be processed (the pixel that is not the first pixel) in the input image in the partial space formed by the basis vectors selected by the selection unit 102
- a second projection vector obtained by projecting a second vector in which values are arranged as elements is obtained.
- One or more second regions are sequentially selected within the search range.
- the distance calculation unit 104 calculates the inter-vector distance between the first projection vector and the second projection vector for each second region.
- the weighted average unit 105 gives a large weight to the pixel value of the second pixel corresponding to the second projection vector in which the distance between vectors calculated by the distance calculation unit 104 is relatively small.
- the weight of each second pixel selected by the projection unit 103 is obtained.
- the pixel value calculated by the weighted average obtained by multiplying the second pixel value by the weight is set as the output pixel value of the first pixel.
- An output image in which the pixel value of the first pixel is replaced with the output pixel value obtained by the weighted average unit 105 is output.
- FIG. 3 is a flowchart showing the operation of the image processing apparatus 100.
- the operation of the inner product calculation unit 101 will be described with reference to FIG.
- FIG. 4A shows a region 401 including the first pixel i.
- FIG. 4B shows a first vector 402 corresponding to the region 401.
- FIG. 4C shows a base vector group 403 and each base vector 4031 excluding a vector having the maximum absolute value of the sum of elements among the base vectors obtained in advance (the first principal component vector in the example of FIG. 4).
- FIG. FIG. 4D is a diagram illustrating an example of the contribution 404 calculated by the inner product calculation unit 101.
- FIG. 4E is a diagram illustrating an example in which the contributions 404 are sorted in descending order.
- the inner product calculation unit 101 calculates the square of the inner product of the first vector 402 in which the pixel values of the pixels in the region 401 including the first pixel i are arranged and each base vector 4031 of the base vector group 403 (S201). ).
- the square of the calculated inner product indicates the contribution 404 of each base vector to the region 401 including the first pixel i.
- the first vector and v i representing the k-th element of v i and (v i) k.
- 2 ⁇ n ⁇ n obtained by removing the first principal component vector from the prepared N basis vectors.
- N ⁇ the contribution pn of the nth basis vector is calculated by Equation (1).
- the selection unit 102 rearranges the contributions 404 calculated by the inner product calculation unit 101 in descending order, and sets the contributions from the top until the sum of the contributions 404 reaches a threshold value indicating a predetermined ratio. to add.
- a threshold value indicating a predetermined ratio. to add.
- each base vector corresponding to each contribution added so far is selected (S202). For example, when the threshold value is 0.7, the threshold value pr is
- the selection unit 102 selects d ⁇ 1 basis vectors ⁇ a n
- each base vector reflects the high correlation with the image in the area.
- a basis vector with a small inner product is likely to be affected by noise. Therefore, by selecting a base vector with a large inner product and calculating the inter-vector distance in the subsequent stage, it becomes possible to calculate the inter-vector distance reflecting the characteristics of the signal in the region, and the influence of noise in calculating the inter-vector distance can be reduced. it is conceivable that.
- the average pixel value in the region has a great influence on the basis vector selection based on the contribution.
- a vector having a large absolute value of the total sum of elements is relatively high compared to a dark area, so that a vector having a large absolute value of the total sum of elements is easily selected.
- An inner product of vectors having a large absolute value of the sum of elements has a large correlation with an average pixel value of the region. This means that if the average brightness is equal in a bright region, the texture portion and the flat portion are not distinguished from each other, which causes unevenness in the bright flat portion and blurring in the bright texture portion.
- the contribution is calculated using a basis vector obtained by removing a vector having the maximum absolute value of the sum of elements from the basis vector group.
- FIG. 5 is a diagram showing the operation of the projection unit 103.
- FIG. 5A is a diagram illustrating an example of regions 4011 to 4013 including the first pixel i, the second pixel j, and the second pixel.
- FIG. 5B is a diagram illustrating a second vector 4021 corresponding to the region including the second pixel.
- FIG. 5C is a diagram illustrating an example of the partial space A generated by the projecting unit 103.
- the projection unit 103 uses the d-1 basis vectors selected from the basis vector group in S202 and the basis vector (the first principal component in the example of FIG. 5) having the maximum absolute value of the sum of elements as a subspace A. And a first vector is obtained by projecting the first vector onto the subspace A. Further, the second vector 4021 in which the pixel values of the pixels in the regions 4011 to 4013 including the second pixel in the input image are arranged is projected onto the partial space A to obtain a second projection vector (S203). When d ⁇ 1 basis vectors are selected in S202, the first projection vector v ′ i obtained by projecting the first vector v i onto the d-dimensional subspace A is
- f i, n is the inner product of the first vector v i and n-th basis vector a n.
- the second projection vector v 'j with a second vector v j and subspace A is
- the distance calculation unit 104 calculates the inter-vector distance on the partial space A between the first projection vector and the second projection vector (S204). Assuming that the first projection vector obtained in S203 is v ′ i and the second projection vector is v ′ j , the distance D (i, j) between v ′ i and v ′ j vector is
- the distance calculation unit 104 determines whether the second projection vector is calculated for the second pixel in the predetermined search range (S205).
- the search range of the second pixel is various such as all pixels in the image including the first pixel, pixels in the vicinity of the second pixel, and in the image not including the first pixel. It may be in the range. For example, in the case of an input image acquired by a sensor, it is effective to select a pixel on the same line as the second pixel.
- the weighted average unit 105 performs weighted average processing that gives a larger weight to the pixel value of the second pixel as the calculated inter-vector distance is smaller, and replaces the pixel value of the first pixel (S206).
- the pixel value of the first pixel to be output ("character P with a hat symbol ⁇ " in the formula is expressed as "P ⁇ " in the text).
- x i ⁇ is the pixel value x j of the second pixel, for example
- ⁇ (i) is a range for searching for the second pixel
- h is a parameter larger than 0.
- the distance is calculated in the space reflecting the feature of the peripheral region of the pixel to be processed, the distance between the vectors of the two regions having different features is reduced. Can be prevented. For this reason, it is possible to prevent an increase in the weight of pixels in regions having different characteristics during weighted averaging. Accordingly, it is possible to reduce noise while maintaining a sharp feeling in the texture portion, and it is possible to reduce noise in the flat portion without causing unevenness.
- the image processing apparatus 600 according to the present embodiment is different from the image processing apparatus 100 according to the first embodiment in that a noise calculation unit 601 that calculates a noise amount is further provided.
- the operation of the selection unit 602 differs according to the amount of noise calculated by the noise calculation unit 601.
- FIG. 6 is a diagram illustrating the image processing apparatus 600.
- the noise amount calculation unit 601 calculates the amount of noise superimposed on the first pixel in the input image.
- the selection unit 502 calculates an inner product of a first vector in which pixel values of pixels in the first region including the first pixel are arranged and a plurality of basis vectors, and calculates a basis vector having a large inner product as the calculated noise. The smaller the amount, the smaller the choice.
- FIG. 7 is a flowchart showing the operation of the image processing apparatus 600.
- the noise amount calculation unit 501 estimates the amount of noise superimposed on the first region including the first pixel to be processed of the input image (S701).
- the amount of noise can be obtained, for example, as a value obtained by multiplying the square root of the average value of the pixel values in the first region by a parameter of 0 or more.
- the standard deviation of the pixel values in the first area can be used as the noise amount.
- the noise amount In addition to estimating and calculating the noise amount from the input image, for example, when the image processing apparatus is incorporated into a digital camera or the like, ISO sensitivity may be used as the noise amount.
- ISO sensitivity When the image processing apparatus is incorporated in an X-ray fluoroscopic apparatus or the like, for example, a value having a negative correlation with the X-ray dose to be irradiated may be used as the noise amount. Further, when the amount of noise generated in these photographing apparatuses is known, the value can be used.
- the selection unit 602 rearranges the contributions calculated in S201 in descending order, and uses the value of a predetermined ratio of the total contributions as a threshold, and adds the contribution from the top until the threshold is reached. Similar to S202, the calculation is performed except for the vector having the maximum absolute value of the sum of the elements. At this time, the threshold is lowered as the amount of noise calculated in S701 increases. When the threshold is reached, each base vector corresponding to each contribution added so far is selected (S703).
- the image processing apparatus of the present embodiment when the amount of noise superimposed on the image is small, the number of basis vectors to be selected is automatically increased, and the signal change in the input image is left faithfully, thereby flattening the texture signal. It is possible to prevent addition to the part. Therefore, the unevenness of the flat portion can be further reduced as compared with the first embodiment.
- the number of basis vectors to be selected is automatically reduced, so that the subspace is constituted only by basis vectors that particularly reflect the state of signal change. Therefore, the influence of noise in calculating the distance between vectors can be effectively reduced, and noise can be removed while maintaining the sharpness of the texture as compared with the case where the number of base vectors is large.
- the basis vector is obtained by excluding the principal component vectors whose eigenvalues corresponding to the principal component vectors obtained in the principal component analysis are smaller than a predetermined threshold. Groups may be configured.
- the contributions of the basis vectors excluding the vector having the maximum absolute value of the sum of the elements in the basis vector group without rearranging the contributions in the inner product calculation unit are used as the principal components.
- the base vectors corresponding to the respective contributions added so far may be selected when the eigenvalues obtained by the analysis are added in order, and when the threshold value is reached.
- the inner product calculation unit may be designed so as to calculate the contribution of a base vector in which the absolute value of the sum of the elements of the base vector group is smaller than a predetermined threshold.
- the contribution of each base vector may be calculated after subtracting the average pixel value of the first region.
- Equation 4 the absolute value of the inner product of the base vector and the first vector in which the pixel values of the pixels existing in the peripheral region including the first pixel are arranged as the contribution of the base vector. May be used. In this case, instead of Equation 1, Equation 9 is used as the contribution.
- Equation 10 may be used instead of Equation 4 as the inter-vector distance between the first projection vector and the second projection vector.
- the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying constituent elements without departing from the scope of the invention in the implementation stage.
- various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment.
- constituent elements over different embodiments may be appropriately combined.
- This image processing apparatus can also be realized by using, for example, a general-purpose computer apparatus as basic hardware. That is, the inner product calculation unit, the projection unit, the distance calculation unit, and the weighted average unit can be realized by causing the processor mounted on the computer device to execute a program. At this time, the image processing apparatus may be realized by installing the above program in a computer device in advance, or may be stored in a storage medium such as a CD-ROM or distributed through the network. Thus, this program may be realized by appropriately installing it in a computer device.
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Abstract
Description
を備える。
図1は、第1の実施形態の画像処理装置100を示すブロック図である。
画像処理装置100は、内積算出部101、選択部102、射影部103、距離算出部104、加重平均部105を備える。
図3は、画像処理装置100の動作を示すフローチャートである。内積算出部101の動作を示す図4を参照しながら説明する。図4(a)は、第1画素iを含む領域401を示す図である。図4(b)は、領域401に対応する第1のベクトル402を示す図である。図4(c)は、あらかじめ得ていた基底ベクトルのうち要素の総和の絶対値が最大のベクトル(図4の例では第1主成分ベクトル)を除いた基底ベクトル群403及び各基底ベクトル4031を示す図である。図4(d)は、内積算出部101が算出した寄与度404の例を示す図である。図4(e)は、寄与度404の高いものから順に並び替えた例を示す図である。
加重平均部105は算出されたベクトル間距離が小さいほど第2の画素の画素値に大きな重みを与える加重平均処理を行い、第1の画素の画素値を置き換える(S206)。出力される第1の画素の画素値(数式中の「ハット記号^の付された文字P」を、本文中では「P^」と表記する)。xi^は第2の画素の画素値xjを用いて例えば
本実施形態の画像処理装置600は、第1の実施形態の画像処理装置100と比べて、さらにノイズ量を算出するノイズ算出部601を設けた点が異なる。ノイズ算出部601が算出したノイズ量に従って選択部602の動作が異なる。
ノイズ量算出部601は、入力画像内の第1の画素に重畳されたノイズ量を算出する。
選択部502は、第1の画素を含む第1の領域の画素の画素値を並べた第1のベクトルと複数の基底ベクトルとの内積を計算し、内積の大きい基底ベクトルを、算出されたノイズ量が大きいほど少なく選択する。
ノイズ量算出部501が、入力画像の処理対象となる第1の画素を含む第1の領域に重畳されたノイズ量を推定する(S701)。ノイズ量は、例えば前記第1の領域の画素値の平均値の平方根に対して0以上のパラメータを乗算した値として得ることができる。または、前記第1の領域の画素値の標準偏差をノイズ量として用いることもできる。なおノイズ量を入力画像から推定して算出するだけでなく、画像処理装置をデジタルカメラ等に組み込む場合は、ノイズ量として例えばISO感度を用いてもよい。画像処理装置をX線透視装置等に組み込む場合は、ノイズ量として例えば照射するX線量と負の相関を持つ値を用いてもよい。また、これらの撮影装置において発生するノイズ量が既知の場合は、その値を用いることができる。
第1および第2の実施形態においては、主成分ベクトルのうち、主成分分析の際に得られたそれぞれの主成分ベクトルに対応する固有値が所定の閾値よりも小さいものを除いたベクトルにより基底ベクトル群を構成してもよい。
第1および第2の実施形態においては、内積算出部において寄与度を並べ替えることなく、基底ベクトル群のうち要素の総和の絶対値が最大のベクトルを除く基底ベクトルの寄与度を、主成分分析によって得られた固有値の順に加算していき、閾値に到達したときにそれまでに加算した各寄与度に対応するそれぞれの基底ベクトルを選択してもよい。
第1および第2の実施形態においては内積算出部において、基底ベクトル群のうち要素の総和の絶対値が所定の閾値よりも小さな基底ベクトルの寄与度を計算するように設計してもよい。また、第1の領域の平均的な画素値を差し引いた上で各基底ベクトルの寄与度を計算してもよい。
第1および第2の実施形態においては、基底ベクトルの寄与度として、第1の画素を含む周辺領域に存在する画素の画素値を並べた第1のベクトルと、基底ベクトルとの内積の絶対値を用いてもよい。この場合、数1に代わり寄与度として数9が用いられる。
101・・・内積算出部、
102、602・・・選択部
103・・・射影部
104・・・距離算出部
105・・・加重平均部
601・・・ノイズ量算出部
Claims (10)
- 画像内の第1の画素を含む第1領域の画素の画素値を要素とする第1のベクトルと、複数の基底ベクトルとの相関の大きさを求める第1の算出部と、
前記複数の基底ベクトルの中から、前記相関の大きさに従って基底ベクトルを選択する選択部と、
前記画像内の第2の画素を含む第2領域を選択し、前記選択された基底ベクトルによって構成される部分空間へ前記第1のベクトルを射影した第1の射影ベクトルと、前記第2領域ごとに当該領域の画素の画素値を並べた第2のベクトルを前記部分空間へ射影した第2の射影ベクトルとを求める射影部と、
前記第2領域ごとに、前記第1の射影ベクトルと、前記第2の射影ベクトルとの距離を算出する第2の算出部と、
前記距離が小さいほど前記第2の画素に大きな重みを与え、前記第2の画素を加重平均して求めた画素値を行い前記距離に応じて前記第1の画素の出力画素値を算出する加重平均部と、
を備えることを特徴とする画像処理装置。
- 前記射影部は、前記第2の画素を含む第2の領域を複数選択することを特徴とする請求項1記載の画像処理装置。
- 前記第1の算出部は、前記第1のベクトルと、前記複数の基底ベクトルとの内積によって前記相関の大きさを求めることを特徴とする請求項1記載の画像処理装置。
- 前記内積算出部は、前記内積の2乗によって計算される寄与度を計算し、
前記選択部は、前記寄与度の合計が所定の閾値に到達するまで前記寄与度が大きい基底ベクトルから寄与度を加算していき、前記閾値に到達するまでに前記寄与度を加算した前記基底ベクトルを選択することを特徴とする請求項2記載の画像処理装置。 - 前記内積算出部は、前記内積の絶対値によって計算される寄与度を計算し、
前記選択部は、前記寄与度の合計が所定の閾値に到達するまで前記寄与度が大きい基底ベクトルから寄与度を加算していき、前記閾値に到達するまでに前記寄与度を加算した前記基底ベクトルを選択することを特徴とする請求項2記載の画像処理装置。 - 前記選択部は、要素の総和の絶対が所定の基準より大きい基底ベクトルを除いた前記複数の基底ベクトルの中から前記相関の大きさに従って前記基底ベクトルを選択し、
前記射影部は、要素の総和の絶対が所定の基準より大きい基底ベクトルと、前記選択された基底ベクトルによって前記部分空間を構成することを特徴とする請求項2記載の画像処理装置。 - 前記第1の画素を含む領域のノイズ量を算出するノイズ量算出部を更に備え、
前記選択部は、前記ノイズ量が多いほど選択する基底ベクトルの数を少なくすることを特徴とする請求項1記載の画像処理装置。 - 前記内積算出部は、事前に一以上の画像から抽出した複数のブロックの主成分分析により基底ベクトルを求めることを特徴とする請求項2に記載の画像処理装置。
- 前記内積算出部は、前記基底ベクトルとして2次元離散ウェーブレット変換の基底を用いることを特徴とする請求項2に記載の画像処理装置。
- 画像内の第1の画素を含む第1領域の画素の画素値を要素とする第1のベクトルと、複数の基底ベクトルとの相関の大きさを求め、
前記複数の基底ベクトルの中から、前記相関の大きさに従って基底ベクトルを選択する、
前記画像内の第2の画素を含む第2領域を選択し、前記選択された基底ベクトルによって構成される部分空間へ前記第1のベクトルを射影した第1の射影ベクトルと、前記第2領域ごとに当該領域の画素の画素値を並べた第2のベクトルを前記部分空間へ射影した第2の射影ベクトルとを求める、
前記第2領域ごとに、前記第1の射影ベクトルと、前記第2の射影ベクトルとの距離を算出する、
前記距離が小さいほど前記第2の画素に大きな重みを与え、前記第2の画素を加重平均して求めた画素値を行い前記距離に応じて前記第1の画素の出力画素値を算出する、
ことを特徴とする画像処理装置。
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JP2014130512A (ja) * | 2012-12-28 | 2014-07-10 | Toshiba Corp | 画像処理装置および画像処理方法 |
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