Image denoising method based on self-adaptive EPLL algorithm

An adaptive, image-based technology, applied in the field of image processing, can solve problems such as unsatisfactory denoising effect and poor denoising effect

Active Publication Date: 2015-11-11
SHENZHEN TISMART TECH CO LTD
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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

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  • Image denoising method based on self-adaptive EPLL algorithm
  • Image denoising method based on self-adaptive EPLL algorithm
  • Image denoising method based on self-adaptive EPLL algorithm

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Embodiment Construction

[0053] refer to figure 1 , the present invention provides a kind of image denoising method based on adaptive EPLL algorithm, comprising:

[0054] S1. Randomly select training samples, and sequentially overlap and divide each training picture, and then use the maximum likelihood estimation algorithm to train the Gaussian mixture model;

[0055]S2. After the image to be processed is overlapped and divided into blocks, calculate the maximum likelihood probability of each obtained image block relative to the Gaussian mixture model, and then classify the image blocks to obtain a set of image blocks corresponding to each Gaussian sub-model;

[0056] S3. For each image block, according to its classification result, combined with the trained Gaussian mixture model, the Wiener filtering method is used to denoise the image block;

[0057] S4. After sparsely representing each image block after denoising, reconstruct the image to be processed;

[0058] S5. Update the parameters of the G...

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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...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
Inventor 蔡念叶倩梁永辉刘根王晗杨志景
Owner SHENZHEN TISMART TECH CO LTD
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