JPH10200755A - Image noise analysis method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide the image noise analysis method by which noise information including noise amount and noise intensity as to a density is detected. SOLUTION: A filtering image 'U' from which noise is eliminated and an original image 'S' are selected alternately in response to a density block (blocks divided by threshold values T1 , T2 ) to which each pixel of the filtering image 'U' belongs, to obtain a discrimination image. The noise information as to each threshold level is detected by subtracting a density histogram HU of the filtering image 'U' from a density histogram HB for the discrimination image. The noise information is detected because the noise of the original image 'S' for the discrimination image in excess of the threshold level and the noise of the filtering image 'U' for the discrimination image that may exceed the threshold level without the smoothing processing are extracted by the subtraction processing.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] 1. Field of the Invention [0002] The present invention relates to an image noise analysis method for analyzing information relating to noise in an image.

[0002]

2. Description of the Related Art Generally, an image processing apparatus optically reads a document by an input scanner or the like, converts the read analog signal into a digital signal, further performs gradation correction, and then performs predetermined image processing. Is going. Therefore, noise may be mixed into image data handled by the image processing apparatus at the time of optical reading or various electrical conversion processes.

When predetermined image processing such as contour enhancement is performed on image data containing such noise, the noise is also enhanced, and the quality of the output image is significantly deteriorated. Becomes

Therefore, in such a case, it is necessary to optimally adjust the intensity of the outline emphasis processing according to the noise mixing state in the image, and it is required to accurately grasp the noise information in the image. .

As a technique for analyzing noise information in an image, a method using a spatial filter has been conventionally implemented. FIG. 14 is a diagram for explaining a conventional noise information analysis filter. As shown in FIG.
The noise information analysis filter is a pixel matrix of a predetermined size (here, a 5 × 5 pixel matrix), and noise information is obtained by scanning this filter on an image. Here, assuming that the coefficient of each pixel constituting the filter is represented as shown in FIG. 14B, the output F _{x, y} from this filter is calculated as follows.

[0006]

(Equation 1)

In Equation 1, S _{x, y} is the density value of the pixel located at the coordinates (x, y) of the original image. "Density value"
Is a value representing the density information of each pixel constituting the image. In the case of a grayscale image, it is a value indicating the density of black and white, and in the case of a color image, for example, "R,
G, B "are the values indicating the respective densities.

The feature of the filter shown in FIG. 14A is that the coefficient (K _0,0 ) of the central pixel (pixel of interest) is positive and the coefficients of the other pixels are negative. This type of filter is a so-called Laplacian filter, which calculates the difference between the density value of the target pixel and the density values of the surrounding pixels. A large filter output is obtained in a portion showing a rapid change in the above, and only a portion having such a high frequency component can be extracted. Although the contour portion in the image also shows a relatively steep change, the degree of steepness is considerably lower (lower frequency) than noise, and it is possible to separate and extract the noise portion.

Therefore, it is possible to extract a noise portion by scanning the filter shown in FIG. 14 (a) on an image, and it is possible to extract a noise distribution in a region of the image, that is, in which region a large amount of noise is distributed. You will be able to recognize that To put it simply, "noise position information"
Can be detected.

[0010]

However, according to the conventional method, it is not possible to detect the noise distribution in the density region, that is, for example, "whether there is much noise in the shadow portion".

Further, even if the presence of noise is detected by the conventional method, it is not possible to detect how much the density value of the noise deviates from the density value of the surrounding area.

Therefore, in the conventional method, it is possible to detect "noise position information", but it is not possible to detect "noise amount and noise intensity for density value". When performing various image processing such as the above-described contour enhancement processing on an image, it is often desirable to know “the noise amount and the noise intensity for the density value”.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and has as its object to provide an image noise analysis method capable of detecting noise information including a noise amount and a noise intensity for a density value.

[0014]

According to an aspect of the present invention, there is provided an image noise analyzing method for analyzing information relating to noise in an image, comprising: A smoothed image creating step of performing a smoothing process on the density value to obtain a smoothed image, (b) a density section setting step of setting a density threshold and setting a plurality of density sections using the density threshold, c) for each pixel, a determining step of determining which of the plurality of density sections the density value in the smoothed image belongs to, and (d) respective densities of the smoothed image and the original image in each pixel. One of the values, a determination image creating step of creating a determination image by selecting according to the result of the determination for each pixel, and (e) a density frequency distribution of the determination image and the smoothed image. By comparing with the density frequency distribution of Detecting noise information at the threshold value.

According to a second aspect of the present invention, in the image noise analysis method according to the first aspect, the plurality of density thresholds are set in the density section setting step based on a density frequency distribution of the smoothed image. Step is included.

According to a third aspect of the present invention, in the image noise analysis method according to the first or second aspect,
(f) further comprising a noise information output step of outputting the noise information.

[0017]

Embodiments of the present invention will be described below in detail with reference to the drawings. In this specification,
First, the principle of the image noise analysis method according to the present invention will be described, and then specific embodiments will be described.

[0018]

The principle of the image noise analysis method according to the present invention will be described with reference to FIGS. FIG.
FIG. 4 is a diagram showing an image profile of an original image, a filtered image, and a determination image. FIG. 2 is a density histogram of the filtered image and the determination image of FIG. Here, as shown in FIG. 1A, the original image is an image containing noise before image processing is performed, and the filtering image is a smoothing filter (see FIG. Is an image from which noise has been removed, obtained by scanning (smoothing processing). The determination image will be described later in detail.

FIG. 2A shows the density histogram of the filtered image. The density histogram of the original image has almost the same distribution. First, the reason for this will be described. Since the original image signal and the noise are mixed in the original image, the frequency of the density value “L ₁ ” in the original image is the sum of the frequency of the original image signal and the frequency of the noise. On the other hand, in the filtered image after the smoothing process,
Since the noise has been removed, the frequency of the density value “L ₁ ” in the filtered image is only the frequency of the original image signal. Here, it should be noted that noise having a value other than the density value “L ₁ ” in the original image is converted into the density value “L ₁ ” of the original image signal as a result of the smoothing process.
That frequency is sometimes added to the frequency of the image signal having the density value “L ₁ ” in the original image. Then, since a similar phenomenon can occur over the entire density range in the image, as a result, the density histogram of the original image and the density histogram of the filtered image have substantially the same distribution.

Next, the determination image shown in FIG. 1B will be described. The image for determination is determined by determining the density value of the filtered image based on a certain threshold “T”,
An image obtained by selecting a filtered image or an original image before and after T ″ and combining them. For example, FIG.
As shown in (b), the density value of the filtered image is equal to the threshold value.
If the value is less than T, a filtering image is selected, and the threshold value “T” is selected.
In the above, the original images are selected, and they are combined to obtain a determination image. Then, features used in the image noise analysis method according to the present invention appear in the density histogram of the image for determination. That is, FIG. 2B shows the density histogram of the determination image, and when compared with the density histogram of the filtered image (or the original image) shown in FIG. 2A, the change of the density distribution before and after the threshold “T” is obtained. Is recognized. This change occurs because the frequency of a portion of the original image constituting the determination image, in which the density value is less than the threshold “T”, is added to the frequency of the filtered image, and FIG. In ()), an increase is observed in the vicinity below the threshold “T”. Conversely, noise is removed from the filtering image that constitutes the determination image, and the portion where the density value is equal to or greater than the threshold value “T” (for the original image before performing the smoothing process, the noise is There is no portion where the density value is equal to or greater than the threshold value “T”). Therefore, the frequency corresponding to the portion is subtracted from the frequency of the original image.
In the area near the threshold “T” or more in FIG.

When the density histogram of the filtered image (or the original image) is subtracted from the density histogram of the image for determination, a density histogram as shown in FIG. 3 is obtained, which indicates noise information near the threshold "T". Becomes In other words, this density histogram is a part of the noise of the original image constituting the determination image, which exceeds the threshold value “T”, and a filtering image constituting the determination image. And the noise portion that would have exceeded.

In the drawing, β indicates the frequency of noise near the threshold value "T", in other words, the density value "
It can be said that this is an index indicating the amount of noise near T ″.
In the figure, α indicates how much the noise density value deviates from the density value of the surrounding area near the threshold “T”, and is an index indicating the noise intensity near the density value “T”. It can be said.

Therefore, if a certain density value is set as a threshold value and the above processing is performed, the noise amount and the noise intensity for the density value can be detected.

Summarizing the above contents, the image noise analysis method according to the present invention provides a method in which the density histogram of the original image and the density histogram of the filtered image have substantially the same distribution. Part of the noise of the original image that has been detected exceeds the threshold, and there is no portion that exceeds the threshold in the selected filtered image. Is going.

[0025]

[Configuration of Apparatus for Performing Noise Analysis] The image noise analysis method according to the present invention is performed based on the above principle, and specific embodiments thereof will be described below.

First, an outline of a noise analyzing apparatus for executing the image noise analyzing method according to the present invention will be described. FIG.
1 is a functional block diagram illustrating a schematic configuration of a noise analysis device that executes an image noise analysis method according to the present invention. The noise analysis device includes an image processing unit 50, an operation unit 10,
It includes a display unit 20, an image input unit 30, and an image output unit 40.

The operation unit 10 is a means for an operator to give an instruction to the noise analyzer, and is constituted by a keyboard and a mouse. As described later, the operation unit 10 also has a function as threshold input means for inputting a threshold. The display unit 20 is a display that allows an operator to check an image to be processed and a message from the image processing unit 50, and has a function of displaying an evaluation keyword or a noise information graph as a noise analysis result. The image input unit 30 is an input unit for reading a document to be processed and inputting an image signal of the read image to the noise analyzer. The image output unit 40 outputs a processed image. Output means (output scanner, imagesetter, etc.).

In the above-described noise analysis apparatus, the operator gives an appropriate command and a threshold value to be set to the image processing unit 50 via the operation unit 10 so that the image input from the image input unit 30 is subjected to noise analysis. Processing can be performed. Then, the operator can visually recognize the evaluation keyword and the noise information graph, which are the analysis results, on the display unit 20.

The image processing section 50 has a storage section 60 and a noise analysis section 70. The storage unit 60 is a RAM that stores an image processing program and an image signal. In addition,
A magnetic disk or the like is connected to the storage unit 60,
A program or data may be read from the magnetic disk.

FIG. 5 is a functional block diagram showing the configuration of the noise analysis unit 70. As shown, the noise analysis unit 70
Are a filtering processing unit 71 and a determination image creation unit 7
2, a filtered image histogram creating unit 73,
A determination image histogram creation unit 74 and a comparison calculation unit 75
, A noise information detection unit 76 and a noise information output unit 77. Each of these processing units represents processing content executed according to the noise analysis processing software,
Details of the processing will be described later.

[0031]

[Noise Analysis Processing Procedure] Next, a noise analysis processing procedure in the noise analysis apparatus having the above configuration will be described. FIG. 6 is a flowchart illustrating the noise analysis processing procedure.

First, the filtering processing unit 71 performs filtering on an image input from the image input unit 30 or an image input in advance and stored on the magnetic disk or the like (hereinafter, these are collectively referred to as “original images”). The process is executed to obtain a filtered image (step S1). As described above, the filtered image is an image from which noise obtained by scanning (smoothing) the original image with a smoothing filter has been removed. The state of the filtering process will be described with reference to FIGS.

FIG. 7 is a diagram for explaining the outline of the filtering process. The pixel to be processed (pixel of interest) OP is scanned while the smoothing filter F is scanned pixel by pixel with the X direction as the main scanning direction and the Y direction as the sub scanning direction with respect to the original image I shown in FIG. It moves and executes the filtering process sequentially. Note that the target pixel OP is a pixel at the center of the smoothing filter F, as shown in FIG. Also,
In the example of FIG. 7B, a smoothing filter F composed of a 5 × 5 pixel matrix is used. Then, according to the coefficient assigned to each pixel component of the smoothing filter F, the pixel of interest OP
Is calculated as the weighted average of the density value of the target pixel OP and the density value of the surrounding pixels, and the obtained value is used as the output of the smoothing filter F as the density value of the target pixel OP after the filtering process. This process is sequentially performed from the pixel at the upper left corner to the pixel at the lower right corner of the image I in the figure to obtain a filtered image.

FIG. 8 is a diagram showing an example of a smoothing filter used in the filtering process. Here, the coefficient of each pixel constituting the smoothing filter of FIG.
, The output U from this smoothing filter
_{x and y} are calculated as follows.

[0035]

(Equation 2)

In Equation 2, S _{x, y} is the density value of the pixel located at the coordinates (x, y) of the original image.

The characteristic of the smoothing filter shown in FIG. 8 is that the coefficients of all the pixels constituting the filter are positive, and the property of averaging local pixel groups, that is, removing high frequency components such as noises by an integration operation. Have. However, FIG.
The filter characteristics are different between the smoothing filter shown in (a) (coefficients are weighted) and the smoothing filter shown in FIG. 8B (all coefficients are equal).
A difference occurs in the detected noise information, but in any case, it is necessary to use a filter for performing an integration operation in the filtering process. Note that the filter is not limited to a 5 × 5 pixel matrix, but a 3 × 5
It may be a three pixel matrix.

Next, the process proceeds to step S2, where a threshold is set. In the example considered here, the operator performs setting input of the threshold via the operation unit 10, but the threshold may be automatically set as described later. Since the threshold value set here is a density value at which noise information should be detected, it is necessary for the operator to set and input a density value at which noise information is to be known as the threshold value. The number of thresholds to be set may be one or more.

Next, proceeding to step S3, the determination image creating section 72 sets a density section. The setting of the density section
When the set threshold is to T _1, T ₂ · · ·, T _n, less than a density value 0 or T _1, T ₁ or T less than _2, similarly to T _n greater than or equal to the maximum density value below follows ( This is performed by dividing into (n + 1) sections.

After setting the density section, step S4
Then, the determination image creation section 72 creates a determination image. As described above, the determination image is an image in which the density value of the filtered image is determined based on the threshold value, and the filtered image and the original image are combined before and after the threshold value. A filtering image or an original image is allocated and configured. The creation of the determination image is determined for each pixel of the filtered image, and the filtered image or the original image is selected according to the density section to which the density value of the pixel belongs. For example, the image signal of the original image (a set of density values of pixels constituting the image) is “S”, and the image signal of the filtered image is “U”.
Then, when 0 ≦ U <T ₁ , “U” is selected, and T ₁ ≦ U <
Select "S" when the T _2, In the same manner, when n is assumed to be odd, selects the "U" when the _{T n-1 ≦ U <T} n ( with the proviso that when n is an even number "S “S” is selected when T _n ≦ U ≦ maximum density value (however, when n is an even number)
U "is selected), whereby a determination image is created.

FIG. 9 is a diagram for explaining how to make a judgment image. In the figure, the horizontal axis indicates the pixel position of the filtered image, and the filtered image or the original image is selected for each pixel according to the density section to which the pixel density value belongs. However, in the figure, only three thresholds T ₁ , T ₂ , and T ₃ are shown for convenience of illustration.

In the above, when the pixel density value of the filtered image is 0 ≦ U <T ₁ , the image signal of the original image “
S "(hereinafter, simply source image" S "called, also the same for other image signals) select, filtered image when T ₁ ≦ U <T _2" select U ", the same procedure is repeated below That is, what is important in the creation of the determination image is to alternately select the filtering image and the original image for each density section with the set threshold value as a boundary. It does not matter which one is selected.

After the determination image is created as described above, the process proceeds to step S5, where a histogram is created. Here, the filtered image histogram creating unit 73 creates a density histogram “H _U ” for the filtered image, and the determination image histogram creating unit 74 creates a density histogram “H _B ” for the determination image. The density histogram created in step S5 is a frequency distribution of density values in each image. Note that, as described in the principle of the present invention, since the density histogram for the filtered image and the density histogram for the original image have almost the same distribution, the filtered image histogram creating unit 73 creates the density histogram for the original image. You may do so.

FIG. 10 is a diagram showing density histograms for the filtered image and the judgment image. As shown in the figure, in the vicinity of the threshold value, on the side where the original image “S” is selected, the density histogram “H _B ” of the determination image has a lower frequency than the density histogram “H _U ” of the filtered image, and the filtering is performed. On the side where the image “U” is selected, the opposite tendency is observed. This is because, as described above in the principle of the invention, before and after the threshold value of the determination image, a part of the noise of the selected original image exists beyond the threshold value, and the threshold value is set in the selected filtered image. This is due to the property that there is no portion that exists beyond. Since the filtered image and the original image are alternately selected for each density section with the set threshold as a boundary, the above phenomenon occurs near each set threshold. Note that FIG.
₂ , only two thresholds T ₁ and T ₂ are displayed for convenience of illustration.

Next, the operation proceeds to step S6, where the comparison operation unit 7
5 performs a comparison operation. Specifically, the density histogram “H _U ” of the filtered image is subtracted from the density histogram “H _B ” of the determination image. FIG. 11 is a diagram illustrating a density histogram after the comparison operation. The meaning of the density histogram indicates “noise information” near the threshold value, as in the principle of the present invention. That is, in the figure, β ₁ and β ₂ are threshold values “T ₁ ” and “T ₂ ”, respectively.
It indicates the frequency of nearby noise, and is an index indicating the amount of noise in the vicinity of the density values “T ₁ ” and “T ₂ ”, respectively.
In the figure, α ₁ and α ₂ are threshold values, respectively.
It indicates to what extent the noise density value near T ₁ ”and“ T ₂ ”deviates from the density value in the surrounding area, and the noise intensity near the density values“ T ₁ ”and“ T ₂ ”respectively. It is an index to be shown.

Then, the noise information detecting section 76 detects noise information, that is, the noise amount and noise intensity at each threshold value (step S7). Here, noise information is detected based on the density histogram of FIG. 11, and the noise amounts “β ₁ ” and “β ₂ ” are frequency differences from a positive frequency peak to a negative frequency peak in the density histogram. Is detected. Further, as the noise intensities “α ₁ ” and “α ₂ ”, a density value difference from a density value corresponding to the frequency of 20% of the positive frequency peak to a density value corresponding to the frequency of 20% of the negative frequency peak is calculated. To detect. The detection of the noise intensity is not limited to this. For example, the detection may be performed based on 10% of the frequency peak.

Next, proceeding to step S8, the noise information output section 77 outputs the detected noise information. The noise information is displayed on the display unit 20 so that the operator can grasp it.
(FIG. 4). FIG. 12 is a diagram illustrating an example of an output form of noise information. As shown, for each threshold,
That is, the noise amount and the noise intensity for each density value are displayed as a graph, and the noise information for the density value can be clearly grasped. If the noise information graph for a standard image is created in advance, the characteristics of the noise distribution and the like become clear by comparison with the noise information graph.

The output form of the noise information is not limited to this. For example, a graph as shown in FIG. 11 may be displayed as direct noise information before being quantified. In addition, the above-mentioned noise amount and noise intensity depend on the number of pixels in the vicinity of the threshold value in the original image in principle (it is considered that there is much noise where the number of pixels is large). Is standardized according to the density frequency distribution of the filtered image and displayed, it does not depend on the number of pixels near the threshold.

Further, as the output form of the noise information,
The present invention is not limited to displaying the graph as described above. For example, an evaluation keyword may be displayed. Specifically, the above-described noise amount and noise intensity of a standard image are measured in advance, stored, and compared with the measured values. Evaluation keywords can be obtained. By displaying such an evaluation keyword, the operator can know which density area of the image has more or less noise, and can perform optimal parameter setting in image processing.

[0050]

[Modifications] Although the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments.
For example, in the above-described embodiment, the operator performs the setting input of the threshold, but this may be automatically performed. Specifically, in FIG. 5, a threshold setting unit is provided between the filtering processing unit 71 and the determination image generation unit 72, and the threshold setting unit generates a density histogram for the filtered image as shown in FIG. . Then, a density value corresponding to the maximum value of the frequency of the density histogram is set as a threshold. Subsequent steps are the same as above.

The density value indicating the maximum value of the frequency in the density histogram is a density value having a relatively high frequency and a large area in an image, and noise information on such a density value is automatically analyzed. It is possible to do. Note that the density value set as the threshold at this time is not limited to the density value corresponding to the maximum value of the frequency, but corresponds to the frequency having a characteristic required by the operator such as the density value corresponding to the maximum value of the frequency. A density value may be set.

[0052]

As described above, according to the first aspect of the present invention, a smoothed image forming step of performing a smoothing process on the density values of the pixels constituting the original image to obtain a smoothed image, A density section setting step of setting a plurality of density sections using the density threshold, a determination step of determining to which of the plurality of density sections the density value in the smoothed image belongs to each pixel, A determination image creation step of selecting one of the density values of the smoothed image and the original image of the pixel in accordance with the determination result for each pixel to create a determination image, Detecting the noise information at the density threshold by comparing the density frequency distribution with the density frequency distribution of the smoothed image.
Noise information near each density threshold can be extracted, and noise information including a noise amount and a noise intensity for a density value represented by each density threshold can be detected.

According to the second aspect of the present invention, the density section setting step includes the step of setting the plurality of density thresholds based on the density frequency distribution of the smoothed image. It is possible to automatically analyze the noise information by using the density value corresponding to the frequency having the density threshold as the density threshold.

According to the third aspect of the present invention, since the apparatus further comprises a noise information output step of outputting the noise information, the operator can clearly grasp the noise information on the density value, and based on the noise information. And accurate image processing can be executed.

[Brief description of the drawings]

FIG. 1 is a diagram illustrating image profiles of an original image, a filtered image, and a determination image.

FIG. 2 is a diagram showing density histograms of a filtered image and a determination image of FIG. 1;

FIG. 3 is a diagram showing a density histogram expressing noise information.

FIG. 4 is a functional block diagram illustrating a schematic configuration of a noise analysis device that executes an image noise analysis method according to the present invention.

FIG. 5 is a functional block diagram illustrating a configuration of a noise analysis unit 70 of FIG. 4;

FIG. 6 is a flowchart illustrating a noise analysis processing procedure.

FIG. 7 is a diagram illustrating an outline of a filtering process.

FIG. 8 is a diagram illustrating an example of a filter used for the filtering process in FIG. 7;

FIG. 9 is a diagram for explaining how a determination image is created.

FIG. 10 is a diagram showing a density histogram for a filtering image and a determination image.

FIG. 11 is a diagram showing a density histogram after a comparison operation.

FIG. 12 is a diagram illustrating an example of an output form of noise information.

FIG. 13 is a diagram showing a density histogram for a filtered image.

FIG. 14 is a diagram for explaining a conventional noise information analysis filter.

[Explanation of symbols]

 Reference Signs List 10 operation unit 20 display unit 70 noise analysis unit 71 filtering processing unit 72 image creation unit for judgment 73 filtered image histogram creation unit 74 image histogram creation unit for judgment 75 comparison operation unit 76 noise information detection unit 77 noise information output unit

Claims (3)

[Claims] 1. An image noise analysis method for analyzing information related to noise in an image, comprising: (a) creating a smoothed image by performing a smoothing process on a density value of a pixel constituting an original image to obtain a smoothed image; And (b) setting a density threshold, and setting a density section using the density threshold to set a plurality of density sections; and (c) for each pixel, the density value in the smoothed image is the plurality of density levels. A determining step of determining which of the sections belongs to, (d) one of the respective density values of the smoothed image and the original image at each pixel, according to the result of the determination for each pixel A determination image creating step of selecting and creating a determination image, (e) comparing the density frequency distribution of the smoothed image with the density frequency distribution of the determination image, thereby obtaining noise information at the density threshold. Detecting. Image noise analysis method characterized. 2. The image noise analysis method according to claim 1, wherein the density section setting step includes a step of setting the plurality of density thresholds based on a density frequency distribution of the smoothed image. Image noise analysis method. 3. The image noise analysis method according to claim 1, further comprising: (f) a noise information output step of outputting the noise information.