Image denoising method based on self-adaptive EPLL algorithm

Abstract

The invention discloses an image denoising method based on self-adaptive EPLL algorithm. The method comprises the following steps: selecting training sample at random, carrying out overlapped blocking on each training image successively, and training a Gaussian mixture model by the maximum likelihood estimation method; respectively calculating the maximum likelihood probability of each image block relative to the Gaussian mixture model after overlapped blocking of images to be processed, and classifying the image blocks to obtain a corresponding image block set of each Gaussian submodel; in allusion to each image block, according to the classification result and with the combination of the well-trained Gaussian mixture model, denoising the image block by a Wiener filtering method; carrying out sparse representation on each denoised image block and then reconstructing images to be processed; and carrying out parameter updating on the Gaussian mixture model according to a classification result of the denoised image blocks. By the method, images can be denoised more accurately and a better image denoising effect can be obtained. The method can be widely applied in the field of image denoising.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image denoising method based on an adaptive EPLL algorithm. Background technique [0002] Glossary: [0003] GMM: Gaussianmixturemodel, Gaussian mixture model; [0004] EM algorithm, ExpectationMaxmizationalgorithm, maximum likelihood estimation algorithm; [0005] EPLL algorithm: ExpectedPatchLogLikelihoodalgorithm, likelihood probability logarithmic expectation algorithm, a recovery method based on prior knowledge of image blocks, by studying the denoising effect of image blocks generated by prior knowledge of different image blocks, to find a better Gaussian mixture model (GMM) as prior knowledge. [0006] Images are the most direct way for human beings to perceive and understand the objective world. Images convey important information, and clear and unpolluted images can ensure the authenticity of information. However, various noises are inevitably introduced in the pr...

AI Technical Summary

Problems solved by technology

In the current technology, there are various methods for image denoising, such as the method of using Gaussian mixture model for denoising. However, the current denoising method using Gaussian mixture model has the problem of poor denoising effect. not ideal

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