De-noising method based on external block autoencoding learning and internal block clustering

A self-encoding and self-encoding model technology, applied in the field of computer vision, can solve problems such as false edges, affecting image visual quality, forcing local self-similarity, etc., and achieves a noise-robust effect

Inactive Publication Date: 2016-08-24
FUZHOU UNIV
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Problems solved by technology

Since the full search is computationally expensive, block matching is usually limited to a small window in the image
In addition, some important texture edges and corners

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  • De-noising method based on external block autoencoding learning and internal block clustering
  • De-noising method based on external block autoencoding learning and internal block clustering
  • De-noising method based on external block autoencoding learning and internal block clustering

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Embodiment Construction

[0027] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0028] The present invention learns the characteristics of external natural image blocks through the automatic coding model in deep learning, and adopts a strategy from coarse to fine to realize global block clustering, and uses a low-rank approximation algorithm for each fine class to realize collaborative filtering between similar image blocks . This method combines the prior information of the external natural image and the internal self-similarity of the image, which can realize the complementarity of global and local information, thereby overcoming the false edge phenomenon caused by the forced local self-similarity in the block matching algorithm, and making the restored Images are more natural and realistic.

[0029] In order for those skilled in the art to further understand the technical solution proposed by the present inventio...

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Abstract

The invention relates to a de-noising method based on external block autoencoding learning and internal block clustering. The method comprises learning block structure features from an external clean natural image block by using an autoencoding model in deep learning, reducing dimensions of a noise image by using the features, achieving block clustering within a whole image range by using a strategy from coarse to fine, constructing a lowrank regular constraint in each class, constructing a global constraint in all classes, establishing a total energy function, and de-noising the target image by means of energy minimization. The method assists internal block clustering de-noising of an image to be tested by using the external natural image block structure information, and solves a problem that a conventional de-noising method is not good in de-noising effect on natural images corroded by Gaussian white noise.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a denoising method based on outer block self-encoding learning and inner block clustering. Background technique [0002] The purpose of image denoising is to restore a potential clean image from the noise map, which is essentially the inverse process of image degradation. As an important low-level vision problem, image denoising techniques have a long history. Early image denoising methods usually use linear or nonlinear local smoothing filtering, such as Gaussian filtering and heterogeneous diffusion. However, most of these edge-preserving algorithms ignore the global information of the image, and it is difficult to achieve the highest level of image denoising. In recent years, a non-local averaging (NLM) method based on the internal self-similarity of images has received a lot of attention. On this basis, many successful denoising algorithms based on image blocks hav...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/002
Inventor 曾勋勋陈飞王灿辉
Owner FUZHOU UNIV
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