A parameter-free image restoration method based on partial differential equations and bm3d

Abstract

The present invention proposes a parameter-free image restoration method based on partial differential equations and BM3D, which belongs to the field of image processing technology. The traditional BM3D improvement method directly uses the existing noise parameter estimation method to obtain a parameter estimate, and then directly brings it into the original BM3D method, but the result is unstable. The image restoration method of the present invention uses partial differential equations to improve the traditional BM3D method. The denoising process and noise variance estimation process have been improved, and are characterized by close integration with partial differential equation preprocessing, which enables the improved BM3D method of the present invention to have high accuracy and high stability. The parameter-free image restoration method based on partial differential equations and BM3D described in the present invention can be applied in various image processing fields.

Description

technical field [0001] The invention relates to a non-parameterized image restoration method based on partial differential equations and BM3D, and belongs to the technical field of image processing. Background technique [0002] The BM3D (Block Matching and 3D Filtering, image block matching algorithm) method is an excellent algorithm in the field of image denoising, and its results have the advantages of high PSNR value, good visual effect, and complete image detail information. However, it also has obvious shortcomings, mainly reflected in the fact that the processing effect of the BM3D method is extremely sensitive to the noise level parameters in the noise image. Specifically reflected in the following aspects: [0003] 1. When the input noise standard deviation is around 40, the PSNR (peak signal-to-noise ratio) value of the BM3D processing result will change abruptly; [0004] 2. When the input noise standard deviation is non-integer, the PSNR value of the BM3D proce...

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Problems solved by technology

[0003] 1. When the input noise standard deviation is around 40, the PSNR (peak signal-to-noise ratio) value of the BM3D processing result will change abruptly;

[0004] 2. When the input noise standard deviation is non-integer, the PSNR value of the BM3D processing result will fluctuate violently;

[0005] 3. Accurate noise standard deviation is not the best input parameter for BM3D method

However, due to the above 1 defect, if the true noise standard deviation is around 40, then the input estimated noise parameters may seriously reduce the restoration quality

Due to the above two defects, the result of noise parameter estimation is generally non-integer, and directly bringing it into the BM3D algorithm will also affect the image restoration result

Due to the above 3 defects, even if some high-precision noise level estimation methods are used to obtain accurate noise level values, directly bringing them into the BM3D method may not necessarily result in optimal processing results

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