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Low-rank approximation fuzzy nucleus estimation method for image blind restoration

A technology of fuzzy kernel and blind restoration, applied in the field of fuzzy kernel estimation of low-rank approximation, which can solve the problem of inaccurate fuzzy kernel estimation.

Active Publication Date: 2014-11-26
XIDIAN UNIV
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Problems solved by technology

Due to too much reliance on these heuristic filters, this method also has the disadvantage of inaccurate blur kernel estimation

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  • Low-rank approximation fuzzy nucleus estimation method for image blind restoration
  • Low-rank approximation fuzzy nucleus estimation method for image blind restoration
  • Low-rank approximation fuzzy nucleus estimation method for image blind restoration

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Embodiment Construction

[0038] refer to figure 1, the specific implementation steps of the present invention are as follows:

[0039] Step 1, set the side length to 3, the spatial domain standard deviation to 0.6, and the value domain standard deviation to 0.7 for preprocessing bilateral filter f, and then perform bilateral filtering on the degraded image y to be processed to obtain edge sharpening and Image y suppressing the influence of noise (1) ;

[0040] Step 2, initialize relevant conditions and parameters, and generate a gradient image matrix;

[0041] 2a) Use the preset blur kernel size (ie the size of the blur kernel matrix) k size Calculate the number of layers n of the pyramid model, according to the size of the fuzzy kernel of the roughest layer (layer 1) and k size The proportional relationship, using bilinear interpolation method to scale y (1) to the coarsest layer (layer 1) i=1, and set the progressive multiple between the gradient image sizes of each layer as Simultaneously ...

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Abstract

The invention discloses a low-rank approximation fuzzy nucleus estimation method for image blind restoration, mainly for solving the problems of how to more accurately realize fuzzy nucleus estimation in a conventional image blind restoration method and how to accordingly restore an ideal image. The realization steps comprise: on one hand, taking a neighbor relation of a gradient image into consideration, and improving an iteration threshold strategy by use of an autoregression (AR) strategy so as to estimate a fuzzy nucleus; on the other hand, enhancing image margin information by use of a heuristic filter to estimate another fuzzy nucleus; afterwards, introducing a low-rank approximation strategy to a fuzzy nucleus estimation process to solve a more reliable fuzzy nucleus; and finally, restoring a clear image by use of an advanced image restoration method. Compared to some conventional methods, the method provided by the invention has the following advantages: PSNR, SSIM and FSIM values are higher, the visual effect is better, fuzziness is effectively removed, more details are maintained, and the designed fuzzy nucleus is also more accurate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to applications in the field of blind image restoration, in particular to a low-rank approximation fuzzy kernel estimation method for blind image restoration, which can be used for various unknown blurs and affected by slight noise. Degraded image for image restoration. Background technique [0002] Blind image restoration refers to removing or alleviating image blur caused by various unknown factors in the obtained digital image, and at the same time, the obtained image will also be affected by some unavoidable noise. Therefore, blind image restoration is an important and challenging research content in image processing. It has very important applications in many aspects, such as medical image processing, material science image processing, public security, history, cultural photo image restoration, surveillance video restoration, and scanned document processing. For this p...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 王爽马文萍蔺少鹏霍丽娜岳波侯彪马晶晶侯小瑾
Owner XIDIAN UNIV
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