CN112446838A - Image noise detection method and device based on local statistical information - Google Patents
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Abstract
The invention provides an image noise detection method and device based on local statistical information, and the technical scheme comprises the following steps: s1, calculating a local statistical information value of each pixel in the image to be measured; s2, judging whether each pixel in the image to be detected is in a flat area or a complex area; s3, calculating a first noise detection threshold of the flat area and a second noise detection threshold of the complex area; s4, under the condition that a certain pixel is in a flat area and the local statistical information value of the pixel is smaller than a first noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel; and under the condition that a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel.
Description
Technical Field
The invention relates to the technical field of image noise detection, in particular to an image noise detection method and device based on local statistical information.
Background
During the acquisition and transmission of images, digital images are often corrupted by impulse noise due to the sensor equipment. Random Value Impulse Noise (RVIN) is one of impulse noises, and its noise pixel value is randomly between 0 and 255, and thus it is difficult to process. In order to perform operations such as contour extraction, region segmentation, and object recognition on the image later, it is necessary to restore the noise image.
At present, mainstream denoising algorithms can be mainly divided into a block matching-based method, a convolutional neural network-based method and a fuzzy rule-based method, and in recent popular denoising algorithms, due to the introduction of the fuzzy rule and the convolutional neural network, although a good filtering effect is achieved, the complexity of the algorithms is increased, the running time is prolonged, and the equipment cost is high.
Disclosure of Invention
The invention aims to provide an image noise detection method and device based on local statistical information, which are simple to realize and have higher detection accuracy and sensitivity compared with the prior art
The invention is realized by the following technical scheme: the invention provides an image noise detection method based on local statistical information, which comprises the following steps:
s1, calculating a local statistical information value of each pixel in the image to be measured;
s2, judging whether each pixel in the image to be detected is in a flat area or a complex area;
s3, calculating a first noise detection threshold of the flat area and a second noise detection threshold of the complex area;
s4, under the condition that a certain pixel is in a flat area and the local statistical information value of the pixel is smaller than a first noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel;
and under the condition that a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel.
Preferably, in step S1, a local statistical information value of each pixel in the image to be measured is calculated, including
Constructing neighborhood by taking any given pixel x in image to be detected as center
Calculate pixel x and neighborhood
Euclidean distance and gray level difference of any pixel y:
calculating pixel x and neighborhood based on Euclidean distance and gray level difference
The similarity of any other pixel y: s (x, y) ═ D (x, y) × I (x, y)
Calculate pixel x and neighborhood
Sum of similarity of all pixels in (1):
zeta x is normalized to be constrained to the [0.1] interval:
will be provided with
StandardizationIs [0.1]]Spacing:
in the formula, D (x, y) is the euclidean distance between the pixel x and the pixel y, I (x, y) is the gray scale difference between the pixel x and the pixel y, and (s, t) represents that the pixel x is in the neighborhood
(m, n) indicates that pixel y is in the neighborhood
Position of (1), σDAdjustment parameter, σ, for Euclidean distanceIZeta x is the sum of the similarities, LS for the adjustment parameter of the gray level differenceXIs the local statistical information value of pixel x.
Preferably, the determining whether each pixel in the image to be measured is in a flat area or a complex area includes:
computing the neighborhood
Estimated mean μ of the intensities of all pixels withinx：
Computing a neighborhood based on the estimated mean
Standard deviation of intensities of all pixels in
Judging whether a given pixel x is in a flat area or a complex area according to the standard deviation:
wherein W1 and W2 are LSyThe weights of (a) and (b) are normalized parameters, TσThreshold for distinguishing whether a pixel is in a complex area or a flat area, LSyIs a neighborhood
Maximum value of local statistical information values of all pixels in the inner, uyIs the gray value of the pixel y having the maximum value of the local statistical information value.
Preferably, the calculating a first noise detection threshold of the flat region and a second noise detection threshold of the complex region includes:
selecting a plurality of flat areas with the size of M in an image to be detected, and judging abnormal pixels and non-abnormal pixels in the flat areas:
estimate the noise level of each region:
obtaining the overall noise level of the image by performing a weighted average operation on the noise level of each region:
calculating a first noise detection threshold for the flat region:
θf＝-0.12σ3+0.07σ2+0.75σ+0.19
calculating a second noise detection threshold for the complex region:
θc＝0.31σ3+0.63σ2+0.52σ+0.03
wherein Q isnIs the number of abnormal pixels, and QcNumber of non-abnormal pixels, d number of flat areas, IxIs the intensity of pixel x, Iyθ is an empirical threshold for the intensity of pixel y.
Preferably, in step S4, LS of the pixel x is compared when the pixel x is in a flat areaxValue and magnitude of first noise detection threshold:
when LSx≤θfWhen pixel x is a noisy pixel, when LSx>θfWhen, pixel x is a clean pixel.
Preferably, LS of the pixel x is compared when the pixel x is in a complex areaxValue and magnitude of the second noise detection threshold:
when LSx≤θcWhen pixel x is a noisy pixel, when LSx>θcWhen, pixel x is a clean pixel.
Preferably, the step S4 further includes, when the pixel x is determined as a noise pixel, performing filtering preprocessing on the image to be detected to obtain a filtered image of the image to be detected, and comparing the pixels x located at the same coordinate of the two images:
when Ix-Ix’|>TPWhen pixel x is a clean pixel, when Ix-Ix’|≤TPWhen, pixel x is a noisy pixel, where Ix' intensity value, T, of the corresponding point of pixel x in the filtered imagePIs a judgment threshold.
Preferably, the θ is in the range of [5,8 ].
Preferably, said T isPThe value is 15.
The second aspect of the present invention provides an image noise detection apparatus, including an obtaining module, further including:
the calculation module is used for calculating the local statistical information value of each pixel in the image to be measured;
the first judging module is used for judging whether each pixel in the image to be detected is in a flat area or a complex area.
The second judgment module is used for judging a certain pixel as a noise pixel under the condition that the pixel is in a flat area and the local statistical information value of the pixel is smaller than the first noise detection threshold value, otherwise, the pixel is a clean pixel;
and under the condition that a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel.
Compared with the prior art, the invention has the following beneficial effects:
according to the image noise detection method and device based on the local statistical information, the probability of whether the pixel is noise or not is represented by the local statistical information value of each pixel in the image to be detected, and the noise pixel and the clean pixel can be screened out by solving the local statistical information value of each pixel in the image and setting a proper threshold value, so that the image noise detection method provided by the invention has high accuracy and sensitivity, and the problem that the method for detecting the pulse noise in the prior art has low accuracy and sensitivity is solved; the invention has simple realization method because the invention does not relate to complex multiplication operation in the realization process, and solves the problem of complex detection method caused by adopting multiplication operation in the prior art.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of an image noise detection method based on local statistical information according to embodiment 1 of the present invention;
fig. 2 is a flowchart of an image noise detection method based on local statistical information according to embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of an image noise detection apparatus according to embodiment 3 of the present invention.
Detailed Description
In order to better understand the technical content of the invention, specific embodiments are provided below, and the invention is further described with reference to the accompanying drawings.
In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present embodiment, "a plurality" means two or more unless otherwise specified.
Example 1
Referring to fig. 1, as a first embodiment of the present application, the present application provides an image noise detection method based on local statistical information, including the following steps:
s1, calculating the local statistical information value of each pixel in the image to be measured, including:
constructing neighborhood by taking any given pixel x in image to be detected as center
Calculate pixel x and neighborhood
Euclidean distance and gray level difference of any pixel y:
calculating pixel x and neighborhood based on Euclidean distance and gray level difference
The similarity of any other pixel y: s (x, y) ═ D (x, y) × I (x, y)
Calculate pixel x and neighborhood
Sum of similarity of all pixels in (1):
zeta x is normalized to be constrained to the [0.1] interval:
herein, the
Presentation pair
The homogenization operation of (1).
By observation, it can be found that ζ x of each pixel in the noisy image is substantially spread at [0, 2.5 ]]In (1). For more convenient, faster processing of data, any pixel can be processed using the following formula
Normalized to [0.1]]Spacing:
in the formula, D (x, y) is a euclidean distance between the pixel x and the pixel y, and I (x, y) is a gray level difference between the pixel x and the pixel y, and both of them decrease as the distance and the gray level difference between the two pixels become larger, which also means that if the gray level difference between the two pixels is large or far, their similarity is small, and even the euclidean distance can be omitted;
(s, t) indicates that pixel x is in the neighborhood
(m, n) indicates that pixel y is in the neighborhood
Position of (1), σDAdjustment parameter, σ, for Euclidean distanceIFor adjusting the gray scale difference, the influence of the two parameters on D (x, y) and I (x, y) can be changed by adjusting the values of the two parameters respectively, wherein zeta x is the sum of the similarities, LSXWhich is a local statistical information value of the pixel x, may represent the probability of whether the pixel is noise or not. If LSXA smaller value indicates a smaller similarity of the pixel x to the pixels in its neighborhood, which means a higher probability that the pixel x is noise.
In a preferred embodiment of this embodimentConstructed neighborhood
A 5 x 5 neighborhood.
S2, judging whether each pixel in the image to be detected is in a flat area or a complex area, including:
computing the neighborhood
Estimated mean μ of the intensities of all pixels withinx：
Computing a neighborhood based on the estimated mean
Standard deviation of intensities of all pixels in
Judging whether a given pixel x is in a flat area or a complex area according to the standard deviation:
wherein W1 and W2 are LSyThe weights of (a) and (b) are normalized parameters, TσThreshold for distinguishing whether a pixel is in a complex area or a flat area, LSyIs a neighborhood
Maximum value of local statistical information values of all pixels in the inner, uyIs the gray value of the pixel y having the maximum value of the local statistical information value.
In a preferred embodiment of this embodiment, the T isσIn the range of [0.3, 8]]
S3, calculating a first noise detection threshold for the flat region, and calculating a second noise detection threshold for the complex region, including:
selecting a plurality of flat areas with the size of M in an image to be detected, and judging abnormal pixels and non-abnormal pixels in the flat areas:
when I isx-Iy>At θ, pixel x is an abnormal pixel, when Ix-IyWhen theta is less than or equal to theta, the pixel x is a non-abnormal pixel, IxIs the intensity of pixel x, IyThe intensity of the pixel y is the gray value of the pixel, and the gray value of the pixel of the image to be detected can be obtained by introducing the image to be detected into a corresponding Matlab program;
in a preferred embodiment of this embodiment, θ is in the range of [5,8 ].
In yet another preferred embodiment of this embodiment, the θ ranges from [0,20 ].
Estimate the noise level of each region:
obtaining the overall noise level of the image by performing a weighted average operation on the noise level of each region:
calculating a first noise detection threshold for the flat region:
θf＝-0.12σ3+0.07σ2+0.75σ+0.19
calculating a second noise detection threshold for the complex region:
θc＝0.31σ3+0.63σ2+0.52σ+0.03
wherein Q isnIs the number of abnormal pixels, and QcD is the number of flat regions, and θ is an empirical threshold.
S4, under the condition that a certain pixel is in a flat area and the local statistical information value of the pixel is smaller than a first noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel;
when a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold, the pixel is determined as a noise pixel, otherwise, the pixel is a clean pixel, and the specific method is as follows:
when pixel x is in a flat region, LS of pixel x is comparedxValue and magnitude of first noise detection threshold:
when LSx≤θcWhen pixel x is a noisy pixel, when LSx>θcWhen, pixel x is a clean pixel.
When pixel x is in a complex area, LS of pixel x is comparedxValue and magnitude of the second noise detection threshold:
when LSx≤θfWhen pixel x is a noisy pixel, when LSx>θfWhen, pixel x is a clean pixel.
Example 2
Referring to fig. 2, as a second embodiment of the present invention, when a clean pixel is on the edge or the contour of the image, the intensity difference between the clean pixel and the pixels in the vicinity thereof is significant, which easily results in that the pixels on the edge and the contour are regarded as noise pixels in the noise detection process, and in order to further improve the accuracy of the detection result, the present invention adds a limit condition to avoid erroneously detecting the edge pixel as the noise pixel on the basis of step S4:
when the pixel x is judged to be a noise pixel, performing median filtering and Gaussian filtering pretreatment on the image to be detected to obtain a filtered image of the image to be detected, and comparing the pixels x located in the same coordinate of the two images:
when Ix-Ix’|>TPWhen pixel x is a clean pixel, when Ix-Ix’|≤TPWhen, pixel x is a noisy pixel, where Ix' intensity value, T, of the corresponding point of pixel x in the filtered imagePIs a judgment threshold.
In a preferred embodiment of this embodiment, the T isPThe value is 15.
Example 3
Referring to fig. 3, as a third embodiment of the present invention, the present invention provides an image noise detection apparatus, including an acquisition module, where the acquisition module is used to acquire a noise image to be detected, and further including:
the calculation module is used for calculating the local statistical information value of each pixel in the image to be measured;
the first judging module is used for judging whether each pixel in the image to be detected is in a flat area or a complex area.
The second judgment module is used for judging a certain pixel as a noise pixel under the condition that the pixel is in a flat area and the local statistical information value of the pixel is smaller than the first noise detection threshold value, otherwise, the pixel is a clean pixel;
and under the condition that a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel.
Further, the present embodiment provides an image noise detection apparatus, which implements the methods described in embodiments 1 and 2 when executed.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An image noise detection method based on local statistical information is characterized by comprising the following steps:
s1, calculating a local statistical information value of each pixel in the image to be measured;
s2, judging whether each pixel in the image to be detected is in a flat area or a complex area;
s3, calculating a first noise detection threshold of the flat area and a second noise detection threshold of the complex area;
s4, under the condition that a certain pixel is in a flat area and the local statistical information value of the pixel is smaller than a first noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel;
and under the condition that a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel.
2. The method for detecting image noise based on local statistic information as claimed in claim 1, wherein in step S1, calculating the local statistic information value of each pixel in the image to be detected comprises
Constructing neighborhood by taking any given pixel x in image to be detected as center
Calculate pixel x and neighborhood
Euclidean distance and gray level difference of any pixel y:
calculating pixel x and neighborhood based on Euclidean distance and gray level difference
The similarity of any other pixel y: s (x, y) ═ D (x, y) × I (x, y);
calculate pixel x and neighborhood
Sum of similarity of all pixels in (1):
zeta x is normalized to be constrained to the [0.1] interval:
will be provided with
Normalized to [0.1]]Spacing:
in the formula, D (x, y) is the euclidean distance between the pixel x and the pixel y, I (x, y) is the gray scale difference between the pixel x and the pixel y, and (s, t) represents that the pixel x is in the neighborhood
(m, n) indicates that pixel y is in the neighborhood
Position of (1), σDAdjustment parameter, σ, for Euclidean distanceIZeta x is the sum of the similarities, LS for the adjustment parameter of the gray level differenceXIs the local statistical information value of pixel x.
3. The method of claim 2, wherein determining whether each pixel in the image to be detected is in a flat region or a complex region comprises:
computing the neighborhood
Estimated mean μ of the intensities of all pixels withinx：
Computing a neighborhood based on the estimated mean
Standard deviation of intensities of all pixels in
Judging whether a given pixel x is in a flat area or a complex area according to the standard deviation:
wherein W1 and W2 are LSyThe weights of (a) and (b) are normalized parameters, TσThreshold for distinguishing whether a pixel is in a complex area or a flat area, LSyIs a neighborhood
Maximum value of local statistical information values of all pixels in the inner, uyIs the gray value of the pixel y having the maximum value of the local statistical information value.
4. The method of claim 3, wherein calculating a first noise detection threshold for flat regions and a second noise detection threshold for complex regions comprises:
selecting a plurality of flat areas with the size of M in an image to be detected, and judging abnormal pixels and non-abnormal pixels in the flat areas:
estimate the noise level of each region:
obtaining the overall noise level of the image by performing a weighted average operation on the noise level of each region:
calculating a first noise detection threshold for the flat region:
θf＝-0.12σ3+0.07σ2+0.75σ+0.19
calculating a second noise detection threshold for the complex region:
θc＝0.31σ3+0.63σ2+0.52σ+0.03
wherein Q isnIs the number of abnormal pixels, and QcNumber of non-abnormal pixels, d number of flat areas, IxIs the intensity of pixel x, Iyθ is an empirical threshold for the intensity of pixel y.
5. The method for detecting image noise based on local statistical information as claimed in claim 4, wherein in step S4, when pixel x is in a flat area, LS of pixel x is comparedxValue and magnitude of first noise detection threshold:
when LSx≤θfWhen, pixel x isNoise pixel, when LSx>θfWhen, pixel x is a clean pixel.
6. The method of claim 5, wherein the LS of the pixel x is compared when the pixel x is in a complex areaxValue and magnitude of the second noise detection threshold:
when LSx≤θcWhen pixel x is a noisy pixel, when LSx>θcWhen, pixel x is a clean pixel.
7. The method according to claim 6, wherein the step S4 further includes, when the pixel x is determined as a noise pixel, performing filtering preprocessing on the image to be detected to obtain a filtered image of the image to be detected, and comparing the pixel x located at the same coordinate of the two images:
when Ix-Ix’|>TPWhen pixel x is a clean pixel, when Ix-Ix’|≤TPWhen, pixel x is a noisy pixel, where Ix' intensity value, T, of the corresponding point of pixel x in the filtered imagePIs a judgment threshold.
8. The method of claim 4, wherein θ is in the range of [5,8 ].
9. The image noise detection method according to claim 7The method of measuring, wherein T isPThe value is 15.
10. An image noise detection device, including obtaining the module, its characterized in that still includes:
the calculation module is used for calculating the local statistical information value of each pixel in the image to be measured;
the first judging module is used for judging whether each pixel in the image to be detected is in a flat area or a complex area.
The second judgment module is used for judging a certain pixel as a noise pixel under the condition that the pixel is in a flat area and the local statistical information value of the pixel is smaller than the first noise detection threshold value, otherwise, the pixel is a clean pixel;
and under the condition that a certain pixel is in a complex area and the local statistical information value of the pixel is smaller than a second noise detection threshold value, judging the pixel as a noise pixel, otherwise, judging the pixel as a clean pixel.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022110804A1 (en) * | 2020-11-24 | 2022-06-02 | 海南大学 | Image noise measurement method and device based on local statistical information |
| CN114764803A (en) * | 2022-06-16 | 2022-07-19 | 深圳深知未来智能有限公司 | Noise evaluation method and device based on real noise scene and storage medium |
| CN114764803B (en) * | 2022-06-16 | 2022-09-20 | 深圳深知未来智能有限公司 | Noise evaluation method and device based on real noise scene and storage medium |
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| Publication number | Publication date |
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| WO2022110804A1 (en) | 2022-06-02 |
| WO2022110804A9 (en) | 2023-09-21 |
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