CN108109123A - A kind of image de-noising method - Google Patents
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Abstract
The invention discloses a kind of image de-noising methods, include the following steps：S01：In pending image, A × a-quadrant centered on pixel F (i, j) is formed, and the variances sigma of pixel F (i, j) and surrounding pixel are obtained in the region2 S(i,j), S02：In A × a-quadrant centered on pixel F (i, j), the variances sigma of the noise estimation value of pixel F (i, j) and the noise estimation value of surrounding pixel is obtained2 N(i,j), S03：Judge the regional extent of pixel F (i, j), S04：If pixel F (i, j) is located at flat site, which is filtered and the pixel value that exports that treated；If pixel F (i, j) is located at details area, the original pixel value that directly exports the pixel is not handled the pixel；The pixel being not handled by the pending image is exported according to initial value, forms treated image.The present invention solves the problems, such as conventional images noise cancellation technique to exist while filtering out noise and also destroys image texture details.
Description
Technical Field
The invention relates to an image processing technology, in particular to an image denoising method.
Background
In current production life, digital images play an extremely important role. In the signal processing and computer vision fields, the data acquired from the image sensor is often contaminated by various noises, such as undesired equipment, data acquisition quantification and external interference, which degrade the data degradation and bring noises during transmission, reception and processing, thereby greatly hindering the analysis and understanding of subsequent images. In order to improve the image quality and improve the effectiveness and reliability of image post-processing, it is necessary to employ noise reduction techniques to filter out image noise. Since existing noise reduction methods often cause image distortion and blurring, noise cancellation remains a very challenging problem. Signals or images in reality are often interfered by various noises in the processes of generation, transmission, reception and processing. Noise distorts the signal or image, severely affecting post-processing and analysis, and even making it difficult to achieve the intended goal. The noise of an image can be classified into system noise and environmental noise from the source, the system noise is noise from the system itself, and the environmental noise is noise from an external environment. In order to obtain a good quality image, it is usually necessary to process and optimize the image by an image denoising algorithm. The conventional image noise reduction methods include the following methods:
(1) median filtering: the method is suitable for filtering salt and pepper noise of the image. However, the filtering effect of image noise with more details, especially with more details of points, lines and spires, is not obvious.
(2) And (3) mean filtering: the mean filtering using the neighborhood averaging method is suitable for removing grain noise in an image obtained by scanning, and although strongly suppressing the noise, it also causes a blurring phenomenon due to averaging.
(3) Adaptive wiener filtering: the method adjusts the output of the filter according to the local variance of the image, the larger the local variance is, the stronger the smoothing effect of the filter is, the better the filtering effect of the adaptive wiener filtering is than the average filtering effect, and the method is useful for keeping the details of the image and other high-frequency parts, but has larger calculation amount.
(4) Wavelet denoising: most of the wavelet coefficients containing signals are reserved, and the details of the image can be well maintained.
In the image denoising process, the traditional algorithms usually adopt the same filtering algorithm for all images, that is, the noise-free part of the images is also processed, so that the noise-free images are interfered by the filtering algorithm, the image quality is affected, meanwhile, the operation amount is increased, and the image processing efficiency is reduced.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an image denoising method, which can distinguish a flat area and a detail area in an image to be processed, and solve the problem that the existing image noise elimination technology destroys image texture details while filtering noise.
In order to achieve the purpose, the invention adopts the following technical scheme: an image denoising method comprises the following steps:
s01: in an image to be processed, an A × A region centered on a pixel F (i, j) is formed, and a variance σ between the pixel F (i, j) and surrounding pixels is found in the region2 S(i,j)Wherein, the image to be processed is composed of M rows and N columns of pixels, M is an integer greater than or equal to 3, N is an integer greater than or equal to 3, A is an odd number greater than 3 and smaller than M and N,
s02: in an A × A region centered on a pixel F (i, j), a variance σ between a noise estimation value of the pixel F (i, j) and noise estimation values of surrounding pixels is obtained2 N(i,j)The method comprises the following specific steps:
s0201: in the image to be processed, an A × A region centered on a pixel F (i, j) is formed, and A in the A × A region centered on the pixel F (i, j) is calculated2Of pixel values of a pixelThe absolute value of the difference between the average value and the pixel value of the pixel F (i, j) is a noise estimation value of the pixel F (i, j), and the noise estimation value of the pixel F (i, j) is used for replacing the pixel value of the pixel F (i, j) in the image to be processed to obtain a noise estimation value image;
s0202: in the noise estimation value image, an A × A region centered on a pixel F ″ (i, j) is formed, and a variance σ between the pixel F ″ (i, j) and surrounding pixels is obtained in the A × A region2 N(i,j)；
S03: judging the area range of the pixel F (i, j), and adopting the following formula:
wherein T1 is a zone control threshold;
s04: if the pixel F (i, j) is located in the flat area, filtering the pixel and outputting a processed pixel value; if the pixel F (i, j) is located in the detail area, the original pixel value of the pixel is directly output without processing the pixel; and outputting the pixels which are not processed in the image to be processed according to the original values to form a complete processed image.
Further, before the noise estimation value image is obtained in step S02, noise is added to each pixel in the image to be processed to obtain a noise image in which an a × a region centered on the pixel F '(i, j) is formed, and an a in the a × a region centered on the pixel F' (i, j) is calculated2Replacing the pixel value of the pixel F '(i, j) in the noise image by the noise estimation value of the pixel F' (i, j) to obtain a noise estimation value image;
further, white gaussian noise having a standard deviation of 30 was added to each pixel in the image to be processed.
Further, in step S04, a gaussian template is used to perform filtering processing on the flat region pixels.
Further, the variance of the pixel F (i, j) and the surrounding pixels in the step S01Wherein,is the pixel value, X, corresponding to the pixel F (i, j) in the image to be processedkIs the pixel value corresponding to the kth pixel in an A multiplied by A area with the pixel F (i, j) as the center, wherein k is more than or equal to 1 and less than or equal to A2。
Further, the variance of the pixel F ″ (i, j) in step S0202 with the surrounding pixelsWherein,for the pixel value, X, corresponding to pixel F ″ (i, j) in the noise estimate imagekIs the pixel value corresponding to the kth pixel in the A × A region with the pixel F ″ (i, j) as the center, wherein k is more than or equal to 1 and less than or equal to A2。
Further, a equals 3.
Further, pixels which are not processed in the image to be processed comprise a first row of pixels, an Mth row of pixels, a first column of pixels and an Mth column of pixels.
Further, the calculation of each pixel in the image to be processed and the noise estimate image is performed according to the order ofGo to the firstIs listed to the firstGo to the firstLine from the secondGo to the firstIs listed to the firstGo to the firstLine from the secondGo to the firstIs listed to the firstGo to the firstThe train follows a serpentine path of the same kind until the completion of the firstAnd denoising the line pixels.
Further, the range of the region control threshold T1 in the step S04 is 1.0-1.8.
The invention has the beneficial effects that: the invention provides an image denoising method for protecting a detail region, which judges a flat region and the detail region by using a noise estimation value, has high processing speed, low hardware implementation resource overhead and convenient implementation, and processes the detail region and the flat region separately, thereby not only obtaining a better effect of the flat region, but also reserving the detail region in the original image to be processed, reserving the texture detail in the image to be processed, and further improving the image denoising effect.
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FIG. 1 is a flowchart of an image denoising method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an image denoising method, which comprises the following steps:
s01: : in an image to be processed, an A × A region centered on a pixel F (i, j) is formed, and a variance σ between the pixel F (i, j) and surrounding pixels is found in the region2 S(i,j)Wherein, the image to be processed is composed of M rows and N columns of pixels, M is an integer greater than or equal to 3, N is an integer greater than or equal to 3, A is an odd number greater than 3 and smaller than M and N,
in the invention, A is an odd number such as 3, 5, 7 and the like, and the denoising method of the invention is not influenced by different values of A, but the smaller the value of A is, the better the denoising effect finally obtained by the invention is. In the invention, in order to obtain a denoised image with a better effect, A is equal to 3, and when A takes other odd values, the subsequent denoising step is not influenced.
In this embodiment, the variance between the pixel F (i, j) and the surrounding pixels is described by taking a as an example of 3In order to facilitate hardware implementation, the variance calculation formula is simplified in the invention, and the following calculation formula is adopted:wherein,is the pixel value, X, corresponding to the pixel F (i, j) in the image to be processedkIs the pixel value corresponding to the kth pixel in a 3 x 3 region with the pixel F (i, j) as the center, wherein k is more than or equal to 1 and less than or equal to 9.
In the step, except that the first row of pixels, the Mth row of pixels, the first column of pixels and the Mth column of pixels in the image to be processed are not calculated, each of the rest pixels corresponds to a variance σ2 S(i,j)And the order of computation does not affect the post-processing results of the present invention. To order the computation, the present invention preferably takes a serpentine path from row 2, column 2 to row 2, column N-1, row 3, column N-1 to row 3, column 2, row 4, column 2 to row 4, column N-1, … …, and so on, of the image to be processed until the pixel computation of row M-1 is completed.
When A takes other values, the same snake-shaped track is adopted for pixel denoising treatment, which specifically comprises the following steps: the calculation of each pixel in the image to be processed, the noise image and the noise estimation value image is performed according to the order ofGo to the firstIs listed to the firstGo to the firstLine from the secondGo to the firstIs listed to the firstGo to the firstLine from the secondGo to the firstIs listed to the firstGo to the firstThe train follows a serpentine path of the same kind until the completion of the firstAnd denoising the line pixels.
S02: in a 3 × 3 region centered on a pixel F (i, j), a variance σ between a noise estimation value of the pixel F (i, j) and noise estimation values of surrounding pixels is obtained2 N(i,j)The method comprises the following specific steps:
s0201: in the image to be processed, a 3 × 3 region centered on the pixel F (i, j) is formed, a mean value of pixel values of 3 × 3 pixels in the 3 × 3 region centered on the pixel F (i, j) is calculated, an absolute value of a difference between the mean value and the pixel value of the pixel F (i, j) is a noise estimation value of the pixel F (i, j), and the noise estimation value of the pixel F (i, j) in the image to be processed is substituted for the pixel value of the pixel F (i, j) in the image to be processed, so that a noise estimation value image is obtained.
Meanwhile, the invention can also adopt the following method to obtain the noise estimation value image: adding noise to each pixel in the image to be processed to obtain a noise image, forming a 3 × 3 area with a pixel F '(i, j) as a center in the noise image, calculating a mean value of pixel values of 3 × 3 pixels in the 3 × 3 area with the pixel F' (i, j) as the center, wherein an absolute value of a difference between the mean value and the pixel value of the pixel F '(i, j) is a noise estimation value of the pixel F' (i, j), and replacing the pixel value of the pixel F '(i, j) in the noise image with the noise estimation value of the pixel F' (i, j) to obtain a noise estimation value image. The noise added to the image to be processed in the present invention can be any noise in the prior art, and in the present embodiment, gaussian white noise with a standard deviation of 30 is added.
It should be noted that when a is 3, the relationship between the noise estimation value image and the image to be processed is: and replacing pixels except for the first row of pixels, the Mth row of pixels, the first column of pixels and the Mth column of pixels in the image to be processed with corresponding noise estimation values, namely forming noise estimation value pixels. When A takes other values, pixels except pixels which are not processed in the image to be processed are replaced by corresponding noise estimation values, namely noise estimation value pixels are formed. Therefore, the image to be processed in the invention is a true image, and the corresponding analog image, noise image and noise estimation value image are all images virtualized for the denoising method in the invention. In order to make the present invention easy to understand, the present invention designates a pixel in the noise estimate value image corresponding to the pixel F (i, j) in the image to be processed as F "(i, j).
S0202: in the noise estimation value image, a 3 × 3 region centered on a pixel F ″ (i, j) is formed, and a variance σ between the pixel F ″ (i, j) and surrounding pixels is obtained in the region2 N(i,j)。
The calculation method thereof is similar to the calculation in step S01. Variance of pixel F "(i, j) with surrounding pixelsIn order to facilitate hardware implementation, the variance calculation formula is simplified in the invention, and the following calculation formula is adopted:wherein,for the pixel value, X, corresponding to pixel F ″ (i, j) in the noise estimate imagekIs the pixel value corresponding to the kth pixel in a 3 x 3 region centered on pixel F ″ (i, j), where 1 ≦ k ≦ 9.
S03: judging the area range of the pixel F (i, j), and the image to be processed has similarity in a flat area and abrupt change in a detailed area. Because of this property, the variance of the gray value of the image in a flat area is small, and the variance in a detail area is large, and according to this characteristic, we judge the area where the image is located according to the following formula.
Where T1 is the zone control threshold.
Wherein, the range of the region control threshold T1 is 1.0-1.8 according to a large amount of test data, and the region control threshold T1 adopted in the invention is 1.4.
S04: if the pixel F (i, j) is located in the flat area, filtering the pixel and outputting a processed pixel value; if the pixel F (i, j) is located in the detail area, the original pixel value of the pixel is directly output without processing the pixel; and outputting the pixels which are not processed in the image to be processed according to the original values.
The flat area can be processed by adopting the filtering technology in the prior art because the flat area has no too many details and edges, the invention adopts a 3 multiplied by 3 Gaussian template for filtering, firstly, the Gaussian template has better effect of protecting image details compared with the mean value filtering, secondly, the Gaussian template is easier to realize by hardware for other filtering, occupies less resources, and secondly, the drying is mainly aimed at Gaussian white noise. The detail regions separated by the method basically cover most of detail textures of the whole image, and in order to protect the detail information and ensure that the separated detail regions are more accurate, the detail regions are not denoised, and the original pixel value output is kept.
The pixels in the flat area in the image to be processed are output after filtering processing, the pixels in the detail area are output without processing, and the pixels which are not processed in the image to be processed are output without processing, wherein the pixels which are not processed comprise the pixels in the first row, the pixels in the Mth row, the pixels in the first column and the pixels in the Mth column in the image to be processed; and finally forming a processed image.
The above description is only a preferred embodiment of the present invention, and the embodiment is not intended to limit the scope of the present invention, so that all equivalent structural changes made by using the contents of the specification and the drawings of the present invention should be included in the scope of the appended claims.
Claims (10)
1. An image denoising method is characterized by comprising the following steps:
s01: in an image to be processed, an A × A region centered on a pixel F (i, j) is formed, and a variance σ between the pixel F (i, j) and surrounding pixels is found in the region2 S(i,j)Wherein, the image to be processed is composed of M rows and N columns of pixels, M is an integer greater than or equal to 3, N is an integer greater than or equal to 3, A is an odd number greater than 3 and smaller than M and N,
s02: in an A × A region centered on a pixel F (i, j), a variance σ between a noise estimation value of the pixel F (i, j) and noise estimation values of surrounding pixels is obtained2 N(i,j)The method comprises the following specific steps:
s0201: in the image to be processed, an A × A region centered on a pixel F (i, j) is formed, and A in the A × A region centered on the pixel F (i, j) is calculated2The noise estimation value image is obtained by replacing the pixel value of the pixel F (i, j) in the image to be processed with the noise estimation value of the pixel F (i, j);
s0202: in the noise estimation value image, an A × A region centered on a pixel F ″ (i, j) is formed, and a variance σ between the pixel F ″ (i, j) and surrounding pixels is obtained in the A × A region2 N(i,j)；
S03: judging the area range of the pixel F (i, j), and adopting the following formula:
wherein T1 is a zone control threshold;
s04: if the pixel F (i, j) is located in the flat area, filtering the pixel and outputting a processed pixel value; if the pixel F (i, j) is located in the detail area, the original pixel value of the pixel is directly output without processing the pixel; and outputting the pixels which are not processed in the image to be processed according to the original values to form a complete processed image.
2. An image denoising method according to claim 1, wherein before the noise estimation value image is obtained in step S02, noise is added to each pixel in the image to be processed to obtain a noise image in which the noise image is formed by pixel F'(i, j) as the center, and calculating A in the A × A region centered on the pixel F' (i, j)2And replacing the pixel value of the pixel F '(i, j) in the noise image by the noise estimation value of the pixel F' (i, j) to obtain a noise estimation value image.
3. An image denoising method according to claim 2, wherein a white gaussian noise with a standard deviation of 30 is added to each pixel in the image to be processed.
4. An image denoising method according to any one of claims 1 to 3, wherein the step S04 is performed by filtering the flat region pixels using a Gaussian template.
5. An image denoising method according to claim 1, wherein the variance of the pixel F (i, j) and the surrounding pixels in step S01 isWherein,is the pixel value, X, corresponding to the pixel F (i, j) in the image to be processedkIs the pixel value corresponding to the kth pixel in an A multiplied by A area with the pixel F (i, j) as the center, wherein k is more than or equal to 1 and less than or equal to A2。
6. An image denoising method according to claim 1, wherein the variance of the pixel F "(i, j) in step S0202 with the surrounding pixelsWherein,for the pixel value, X, corresponding to pixel F ″ (i, j) in the noise estimate imagekIs the pixel value corresponding to the kth pixel in the A × A region with the pixel F ″ (i, j) as the center, wherein k is more than or equal to 1 and less than or equal to A2。
7. An image denoising method according to any one of claims 1, 2, 5 and 6, wherein a is equal to 3.
8. The method of claim 7, wherein the unprocessed pixels in the image to be processed comprise a first row of pixels, an Mth row of pixels, a first column of pixels, and an Mth column of pixels.
9. An image denoising method according to claim 1, wherein the calculation of each pixel in the image to be processed and the noise estimate image is performed according to the order of the second orderGo to the firstIs listed to the firstGo to the firstLine from the secondGo to the firstIs listed to the firstGo to the firstLine from the secondGo to the firstIs listed to the firstGo to the firstThe train follows a serpentine path of the same kind until the completion of the firstAnd denoising the line pixels.
10. An image denoising method according to claim 1, wherein the region control threshold T1 in step S04 is in the range of 1.0-1.8.
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| CN114742727A (en) * | 2022-03-31 | 2022-07-12 | 南通电博士自动化设备有限公司 | Noise processing method and system based on image smoothing |
| CN117423113A (en) * | 2023-12-18 | 2024-01-19 | 青岛华正信息技术股份有限公司 | Adaptive denoising method for archive OCR (optical character recognition) image |
| CN117423113B (en) * | 2023-12-18 | 2024-03-05 | 青岛华正信息技术股份有限公司 | Adaptive denoising method for archive OCR (optical character recognition) image |
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