JP4571602B2 - Noise reduction apparatus and method - Google Patents

Description

  The present invention relates to a noise reduction apparatus and method.

  Image sensors such as CCD sensors or MOS sensors used in digital cameras and the like are steadily increasing in pixel count and sensitivity. Therefore, the influence of noise has become a problem.

  In the prior art, when removing noise, attempts have been made to extract and remove noise accurately without losing information of the original image as much as possible, and the location of the image to be noise-removed is the edge portion of the image. It is determined whether it is noise or noise, and noise removal is not performed on the edge portion (for example, Patent Document 1). In addition, the correlation with the surrounding pixels of the target portion is determined, and the amount of noise removal is changed according to the correlation in the horizontal and vertical directions so as not to affect the edge of the image (for example, Patent Document 2).

  In addition, images were classified into frequency bands, and noise was removed efficiently by changing the amount of noise removal according to each frequency band, so that the bands other than the noise were not affected as much as possible. (For example, patent document 3).

  In addition, a conventional technique has been proposed in which a plurality of infrared cut filters with different infrared removal wavelengths are switched to achieve both high sensitivity and color reproducibility (for example, Patent Document 2).

Japanese Patent Laid-Open No. 2001-76134 (FIG. 3) JP 2001-189944 (paragraph 0036) JP 2006-50109 (paragraph 0035)

  However, in the related arts disclosed in Patent Document 1 and Patent Document 2, when noise increases, an error for discriminating an edge increases, so that the effect of noise removal is not sufficient or the original image is affected. was there.

  Furthermore, even in the prior art disclosed in Patent Document 3, since noise is almost always present over all frequency bands, there is a problem that a sufficient noise removal effect cannot be obtained.

The present invention
A color discriminating means for inputting a plurality of different color signals and discriminating a color of a target image from the plurality of colors;
Noise removing means provided for each of the plurality of color signals, capable of removing noise from the color signals and changing the amount of noise removal;
A noise removal amount setting means for determining a noise removal amount of the noise removal means according to a result of the discrimination by the color determination means;
The noise removal amount setting means increases a noise removal amount of a color signal that is not a main component, rather than a noise removal amount of a color signal that is a main component of the color determined by the color determination unit,
The color discriminating means is composed of a plurality of comparing means, and when one color signal is larger than the other color signals and the difference is not less than a predetermined reference value among the plurality of color signals, That one color is determined as the main component of the target image,
Noise reduction characterized in that when two color signals are larger than the other color signals and the difference is greater than or equal to a predetermined reference value, the two colors are determined as the main components of the target image. Providing equipment.

  According to the present invention, there are provided a noise reduction device and a noise removal method capable of obtaining a high noise removal effect without losing image information.

Embodiment 1 FIG.
FIG. 1 is a schematic configuration diagram of a noise reduction apparatus according to Embodiment 1 of the present invention. In FIG. 1, a plurality of color signals are input from an input terminal 1. In this embodiment, a plurality of color signals are three types of R signal, G signal, and B signal. Each color signal is input to the color discrimination means 2. The color discriminating means 2 discriminates what is the main component of the signal color at the pixel position where noise is to be removed or in the vicinity of the pixel position. The configuration and operation of the color discrimination means 2 will be described later in detail.

  On the other hand, the R signal (R), G signal (G), and B signal (B) are input to the R noise removing unit 4r, the G noise removing unit 4g, and the B noise removing unit 4b, respectively. (Hereinafter, the R noise removing means, the B noise removing means, and the G noise removing means may be collectively referred to as noise removing means.) The noise removing means 4r, 4g, and 4b have the same configuration and are based on input signals. Noise can be removed, and the amount of noise removal can be changed. The noise removal amount setting means 3 changes the noise removal amounts of the noise removal means 4r, 4g, and 4b according to the color determined by the color determination means 2 (that is, according to the determination signal Sp).

The color discriminating means 2 has means for comparing the magnitudes of the input color signals, and discriminates what is the main component of the color of the signal from the comparison relationship. For example, when the magnitude relationship of signals is R>G> B, the difference between R and G is equal to or greater than a predetermined reference value (threshold value) k1, that is, R is equal to or greater than a value obtained by adding threshold k1 to G. (R ≧ G + k1), the main component of the signal is determined to be R.
Further, when R is smaller than the value obtained by adding k1 to G (R <G + k1) and G is equal to or greater than the value obtained by adding a predetermined reference value (threshold value) k2 to B (when G ≧ B + k2) ) Determines that the main components of the signal are R and G.
Further, when R is smaller than G plus threshold k1 (R <G + k1) and G is smaller than B plus threshold k2 (G <B + k2), the signal The main components are all R, G, and B color signals.
By the above comparison means, it is determined whether one color signal having the main components of the three color signals, two color signals, or three color signals, and which color signal is the main component. The relationship of R>G> B is an example, and there are seven types of combinations of the main components: R, G, B, R and G, R and B, G and B, and R and G and B.

  FIG. 2 shows a configuration example of the color discrimination means 2. First, R, G, and B are input to the first comparison unit 11. The first comparing means 11 compares and discriminates the first largest signal M1, the second largest signal M2, and the third largest signal M3 in R, G and B. Next, the first largest signal M1 and the second largest signal M2 are compared by the second comparison means 12, and it is determined whether or not the difference is equal to or greater than a threshold value k1. Further, the second largest signal M2 and the third largest signal M3 are compared by the third comparing means 13, and it is determined whether or not the difference is not less than a threshold value k2. Based on the comparison results of the first comparison unit 11, the second comparison unit 12, and the third comparison unit 13, the principal component determination unit 14 determines the principal component of the color at the position from which noise is to be removed, A signal Sp indicating the discrimination result is output.

The noise removal amount setting means 3 is an R noise removal means 4r, a G noise removal means 4g, and a B noise removal means so as to remove a large amount of color noise corresponding to the complementary color of the main component color determined by the color determination means 2. The amount of noise removal 4b is set. Here, when the main component is R, the complementary colors are G and B. When the main component is G, the complementary colors are B and R. When the main component is B, the complementary color is R. And G, when the main components are R and G, the complementary color is B, when the main components are G and B, the complementary color is R, and when the main components are B and R, The complementary color is considered to be G.
The relationship between the determined principal component and the noise removal amount is as shown in the following table.

In the above table, “large” and “small” representing the noise removal amount are relative expressions, for example,
When it is determined that R is the main component, the noise removal amount of B and G is larger than the noise removal amount of R, and when it is determined that G is the main component, it is larger than the noise removal amount of G. When the noise removal amount of R and B is increased and it is determined that B is the main component, the noise removal amount of R and G is set larger than the noise removal amount of B, and R and G are the main components. When it is determined that there is, the noise removal amount of B is larger than the noise removal amount of R and G, and when it is determined that R and B are the main components, the noise removal amount of R and B is When the noise removal amount of G is increased and it is determined that G and B are the main components, this indicates that the noise removal amount of R is made larger than the noise removal amounts of G and B.
Also, the amount of R noise removed when R is determined to be the main component, the amount of G noise removed when G is determined to be the main component, and the B when B is determined to be the main component. Noise removal amount,
R and G noise removal amounts when R and G are determined to be main components, R and B noise removal amounts when R and B are determined to be main components, and G and B are main components The noise removal amounts of G and B when determined to be R, G, and B when R, G, and B are determined to be main components are substantially equal to each other, and R is the main noise removal amount. B and G noise removal amounts when determined to be components, R and B noise removal amounts when G is determined to be a main component, and R when B is determined to be a main component And G noise removal amount, B noise removal amount when R and G are determined to be main components, G noise removal amount when R and B are determined to be main components, G and B The noise removal amounts of R when it is determined that is a main component are substantially equal to each other Represents that no.

  The noise removing means 4r, 4g, and 4b are means for removing noise from the input signal, and the method is not particularly limited in the present invention. For example, in the case of averaging processing by LPF, a pixel for noise removal is selected. As the center, the averaging with the surrounding pixels is performed. When the weighting ratio of the central pixel is increased during the addition averaging, the noise removal rate is lowered, and when the simple averaging is performed (or the weighting difference is reduced), the noise removal rate is increased. Further, if the number of peripheral pixels for which the averaging is performed is increased, the noise removal amount is increased, and if it is decreased, the noise removal amount is decreased. In addition, there is another method using a non-linear filter or the like as the noise removing means 4r, 4g, 4b. An example of a filter is given. In this case, if the value of the threshold value k3 that determines the clipping amount is small, the noise removal amount is small, and the noise removal amount increases as the value of k3 increases. In the present invention, the noise removing means in the noise removing means 4r, 4g, 4b can be effective in any means as long as it can change the amount of noise removal.

  The noise removal amount may be a fixed removal amount or may be a continuous change amount according to the magnitude relationship of the main components.

  Furthermore, in color discrimination, if the input color signal is determined for a specific pixel, the principal component of the color of the pixel targeted for noise removal is determined. For example, the determination differs for each individual pixel. If it is avoided, if the average values of R, G, and B of a plurality of pixels (for example, 4 pixels in the horizontal and vertical alignment) are determined, the color is determined including the neighboring pixels.

  Here, the principle of the present invention will be described. Noise means that a signal that is different from the original signal is added to the original image that should be originally obtained, thereby impairing the quality of the image or not being able to correctly recognize the original signal. is there. For noise added as a predetermined position or as a predetermined frequency, it is only necessary to detect and remove the signal. However, shot noise generated from the dark current of the image sensor and amplifier noise generated from the circuit are transmitted by the image. In most cases, it is difficult to distinguish between noise and original signal in terms of frequency. Therefore, it is important to efficiently remove noise using human visual characteristics.

  For example, as shown in FIGS. 3A to 3C, a human is a noise generated in a high frequency region in an image (a part indicated by a symbol Na in FIG. 3A, and is shown in FIG. Noise generated in a lower frequency region (shown by the symbol Nb in FIG. 3 (A) and enlarged in FIG. 3 (C)) than that generated in the lower frequency region I am worried about. This characteristic is used to eliminate noise almost at the edge of an image having a high frequency in the image, and to perform much noise removal at a portion without an edge.

  On the other hand, as shown in FIG. 4, the noise itself is more worrisome in terms of visual characteristics than low-frequency noise (FIG. 4B) than high-frequency noise (FIG. 4A). Therefore, if the image is divided into frequency bands and the amount of noise removal in the low-frequency image region is increased, the quality of the image quality is relatively easily maintained.

  However, all of these conventional techniques consider that signal errors other than the original signal are added with noise, and are subject to removal. On the other hand, in a hard copy, a printer, or the like, there is a dither process for rearranging the original signals as a process for increasing the apparent gradation in an image with few gradations. In the image after the dither processing, the signal value at the target pixel position is different from the original true value. In this case, however, the image quality is not deteriorated but the image quality is increased. Thus, it can be said that the error from the true value of the image is not necessarily noise that impairs the quality of the image quality and should not necessarily be removed.

  In color images, noise often appears as a noise that is different from the original color, as represented by color noise rather than the amount of noise signal itself. . For example, when noise occurs in a red flower, it is visually inferior to have a green or blue signal appear in the red flower rather than displaying the R signal with an error from the original signal. It is determined as an image. In addition, in a red flower image, naturally, a lot of information as an image such as a signal modulation component is included in the red color. Therefore, in the case of noise that is difficult to separate from the original image, the amount of removal of R noise is reduced and the amount of removal of G noise and B noise is increased rather than losing information of the original signal in an attempt to largely remove R noise. It is possible to minimize the degradation of the image quality and efficiently remove the noise that degrades the image quality.

  FIG. 5 is a flowchart showing a noise removal method according to the present invention. First, the magnitude relationship of signals is determined from R, G, and B signals (Step 1). Here, the largest signal is M1, the second largest signal is M2, and the third largest signal is M3. Next, it is determined whether or not the difference between the first largest signal M1 and the second largest signal M2 is greater than or equal to the threshold value k1 (Step 2). If the difference is larger, the main component of the signal color is identified as A (Step 3). . Next, if the difference between the first largest signal M1 and the second largest signal M2 is smaller than k1, whether the difference between the second largest signal M2 and the third largest signal M3 is greater than or equal to the threshold k2. It is discriminated (Step 4), and if it is larger, the main components of the signal color are discriminated as A and B (Step 5). Next, when the difference between the second largest signal M2 and the third largest signal M3 is smaller than k2, the principal components of the color of the signal are discriminated from M1, M2 and M3 (Step 6). An R noise removal amount, a G noise removal amount, and a B noise removal amount are determined in accordance with the determined principal components of the color (Step 7). Next, the noise of each color signal is removed according to the determined noise removal amount (Step 8).

  In this embodiment, a plurality of color signals are described as three types of color signals of R, G, and B signals. However, in the camera, Ye (yellow), Mg (magenta), Cy (cyan), and G (green) are also used. There are also 4 types of cases. In this case as well, it is needless to say that the same effect can be obtained by determining the principal component color by the color discrimination means from the above four kinds of magnitude relationships and determining the noise removal amount in each color signal. . In printing devices, Ye (yellow), Mg (magenta), Cy (cyan), and K (black) are used as color signals in signal processing. The same effect can be obtained by providing the removal amount.

It is a block diagram of a noise reduction apparatus. It is a block diagram of a color discrimination means. (A)-(C) is a figure explaining the human visual characteristic with respect to noise. (A) And (B) is a figure explaining the human visual characteristic with respect to noise. It is a figure explaining the method of noise removal.

Explanation of symbols

1 input terminal, 2 color discrimination means, 3 noise removal amount setting means, 4r R noise removal means, 4g G noise removal means, 4b B noise removal means.

Claims (4)

A color discriminating means for inputting a plurality of different color signals and discriminating a color of a target image from the plurality of colors;
Noise removing means provided for each of the plurality of color signals, capable of removing noise from the color signals and changing the amount of noise removal;
A noise removal amount setting means for determining a noise removal amount of the noise removal means according to a result of the discrimination by the color determination means;
The noise removal amount setting means increases a noise removal amount of a color signal that is not a main component, rather than a noise removal amount of a color signal that is a main component of the color determined by the color determination unit,
The color discriminating means is composed of a plurality of comparing means, and when one color signal is larger than the other color signals and the difference is not less than a predetermined reference value among the plurality of color signals, That one color is determined as the main component of the target image,
Noise reduction characterized in that when two color signals are larger than the other color signals and the difference is greater than or equal to a predetermined reference value, the two colors are determined as the main components of the target image. apparatus. A color discriminating means for inputting a plurality of different color signals and discriminating a color of a target image from the plurality of colors;
Noise removing means provided for each of the plurality of color signals, capable of removing noise from the color signals and changing the amount of noise removal;
A noise removal amount setting means for determining a noise removal amount of the noise removal means according to a result of the discrimination by the color determination means;
The noise removal amount setting means increases a noise removal amount of a color signal that is not a main component, rather than a noise removal amount of a color signal that is a main component of the color determined by the color determination unit,
The plurality of color signals are R, G, and B signals,
The noise removal amount setting means is configured such that the color of the image of the portion targeted for noise removal by the color discrimination means is
When it is determined that R is the main component, the noise removal amount of B and G is made larger than the noise removal amount of R,
When it is determined that G is a main component, the noise removal amount of R and B is made larger than the noise removal amount of G,
When it is determined that B is the main component, the noise removal amount of R and G is made larger than the noise removal amount of B,
When it is determined that R and G are the main components, the noise removal amount of B is larger than the noise removal amount of R and G,
When it is determined that R and B are main components, the noise removal amount of G is made larger than the noise removal amount of R and B,
A noise reduction apparatus characterized in that when it is determined that G and B are principal components, the noise removal amount is set so that the R noise removal amount is larger than the G and B noise removal amounts. Input a plurality of different color signals, determine the color of the target image from the plurality of colors,
For each of a plurality of color signals, noise is removed from the color signal, and the amount of noise removal is changed,
Depending on the result of color discrimination, the noise removal amount for noise removal is determined,
The noise removal amount of the color signal that is not the main component is made larger than the noise removal amount of the color signal that is the main component of the color whose color is determined,
In determining the main component of the color,
When one color signal is larger than the other color signals and the difference is greater than or equal to a predetermined reference value, the one color is determined as the main component of the target image. ,
A noise reduction method in which when two color signals are larger than other color signals and the difference is equal to or greater than a predetermined reference value, the two colors are discriminated as main components of a target image. Input a plurality of different color signals, determine the color of the target image from the plurality of colors,
For each of a plurality of color signals, noise is removed from the color signal, and the amount of noise removal is changed,
Depending on the result of color discrimination, the noise removal amount for noise removal is determined,
The noise removal amount of the color signal that is not the main component is made larger than the noise removal amount of the color signal that is the main component of the color whose color is determined,
The plurality of color signals are R, G, and B signals, and the color of the image of the portion to be subjected to noise removal is
When it is determined that R is the main component, the noise removal amount of B and G is made larger than the noise removal amount of R,
When it is determined that G is a main component, the noise removal amount of R and B is made larger than the noise removal amount of G,
When it is determined that B is the main component, the noise removal amount of R and G is made larger than the noise removal amount of B,
When it is determined that R and G are the main components, the noise removal amount of B is larger than the noise removal amount of R and G,
When it is determined that R and B are main components, the noise removal amount of G is made larger than the noise removal amount of R and B,
A noise reduction method characterized in that when it is determined that G and B are principal components, the R noise removal amount is made larger than the G and B noise removal amounts.

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