A general image noise estimation method based on self-similarity measure

A technology of image noise and noise estimation, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of complex algorithm theory, reduced accuracy of image noise estimation, unfavorable application in practice, etc., and achieve the effect of simple theory

Active Publication Date: 2019-03-29
TAISHAN UNIV
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Problems solved by technology

[0003]The above methods are limited to estimating a single type of image noise, but the noise model in real images often does not only contain a single type of noise, but consists of multiple Mixture of types of noise
The above noise estimation is basically for additive Gaussian noise, but for multiplicative noise, coherent speckle noise, etc., it will often fail; many real images do not have many flat areas, and the noise estimation accuracy for images with fewer flat areas will be reduced. will greatly reduce
These algorithm theories are very complicated, so it is not conducive to the real application in practice
However, in actual image denoising, accurate noise estimation must be performed on the image to effectively achieve image denoising. At present, there is no general and high-precision image noise estimation method.

Method used

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  • A general image noise estimation method based on self-similarity measure
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  • A general image noise estimation method based on self-similarity measure

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Embodiment 1

[0031] refer to figure 1 As shown, a general image noise estimation method based on self-similarity metric, including the following steps:

[0032] S1. Generate an image with a constant pixel value, and add Gaussian noise with known noise intensity standard deviation to the image to form a standard noisy image, and perform block matching and row matching on the noisy image and calculate the average minimum distance metric D mean , to establish a robust correspondence between the average minimum distance metric and the noise standard deviation through statistical methods;

[0033] S2. Using the corresponding relationship established in S1 to perform noise estimation on the actual noisy image, obtain the noise standard deviation σ1 of the actual noisy image, and simultaneously generate the minimum distance metric mapping image of the actual noisy image;

[0034] S3. Use σ1 in S2 and the generated minimum distance metric mapping image to determine the smooth area in the image, ...

Embodiment 2

[0038] refer to figure 2 as shown, figure 2 Be the method flowchart of the general image noise estimation method based on self-similarity measure of the present invention;'

[0039] The first step: image block matching and row matching calculate the average minimum distance metric;

[0040] refer to image 3As shown, first generate a 256×256 grayscale image with a pixel value of 0.5, the pixel value can also be set as other parameters according to the needs of the image, add Gaussian noise with a noise intensity of standard deviation of 1.0 to the image, and execute the generated For block matching of noisy images, an image block of size N_step is used to extract an image block of size N_step as a reference block, and then block matching is performed in a neighborhood of size NS×NS centered on the reference block to obtain a number of N2 Similar to the image blocks, all the matching image blocks are column-scanned and spliced ​​into a matrix M with a size of (N1×N1)×N2, a...

Embodiment 3

[0052] refer to image 3 As shown, the constant value image and the noisy image in the noise estimation experiment of this embodiment; Figure 4 is an actual noisy image and the distance metric mapping image obtained from it;

[0053] In the research of image denoising in this embodiment, Figure 5 The 10 standard images shown are used MATLAB software to carry out noise addition and noise estimation experiments to the image, adding noise of different intensities to the image each time, and then using the method in Example 2 to noise the image with noise. estimate. Figure 6 is the image noise estimation result of this embodiment. It can be seen from the data in the table that the noise estimation result of this embodiment is very accurate, and can be completely applied to image denoising practice to realize blind image denoising. The estimated results are basically a bit high, because there are more or less noises in the original image.

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Abstract

The invention discloses a general image noise estimation method based on self-similarity measure, which includes such steps as S1, generating an image with constant pixel value, adding Gaussian noisewith known standard deviation of noise intensity to the image, performing block matching and row matching on the noisy image, and calculating average minimum distance metric Dmean; 2, obtainING that correspondence relationship between the noise standard deviation and the stability of the distance metric according to the mean minimum distance metric Dmean; S3, estimating the noise intensity sigma 1according to the obtained correspondence relation for the actual noisy image and simultaneously generating a distance metric mapping image; S4, judging the smooth region in the image by using the result and the obtained distance metric map image, estimating the noise only in the smooth region of the image, and finally obtaining the accurate noise intensity sigma through two-step iteration. The invention does not need any image transformation, only carries out image self-similarity measurement in the spatial domain, the theory is very simple, and the whole process only uses the Euclidean distance to calculate the self-similarity of the image.

Description

technical field [0001] The present invention relates to the technical field of image noise estimation, and more specifically, to a general image noise estimation method based on self-similarity measure. Background technique [0002] In real life and scientific research, different types of noises with different intensities will inevitably be introduced during the acquisition process of various images. Add noise of known type and known intensity, and then use your own technology to perform image denoising experiments. However, the noise intensity in real images is unknown. In order to achieve effective image denoising, it is necessary to estimate the noise accurately in advance. The existing image noise estimation methods mainly include methods based on wavelet transform and discrete cosine transform, and image noise estimation is realized by counting the transformation coefficients after image transformation. There are two good methods for recent image noise estimation: one...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/223G06T5/00G06T3/40
CPCG06T3/4038G06T5/002G06T7/223
Inventor 侯迎坤侯昊杨洪祥梁凤鸣
Owner TAISHAN UNIV
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