Image denoising method based on biological vision receptive field mechanism

A biological vision and receptive field technology, applied in the field of computer vision, can solve the problems of complex calculation, blurred image edge information, and large amount of calculation, and achieve the effect of simple calculation, good edge information and structural information, and high signal-to-noise ratio.
CN104966271AInactive Publication Date: 2015-10-07UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Publication Date
2015-10-07
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses an image denoising method based on a biological vision receptive field mechanism. Specifically, the method comprises the steps of noise judging, noise processing and result image brightness correcting. The method in the invention first makes a vision mechanism introduced into the image denoising research field. The noise position and size can be determined after a noise image passes through a receptive field template. The noise is suppressed in an adaptive way through a corresponding receptive field model according to determined noise. The brightness of a result graph is corrected according to the brightness of the noise image. The image acquired after the result graph brightness correction serves as a noise image which will undergo iteration processing until an optimum denoising effect is achieved. The image denoising method which can perform effective denoising can further make the image structure information well kept. A higher signal to noise ratio and great subjective effects can be achieved.
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Description

technical field

[0001] The invention belongs to the technical field of computer vision, in particular to an image denoising method. Background technique

[0002] Images are an important source of information for people to obtain. However, due to various reasons when acquiring images, noise will be mixed into the image, which will lead to the degradation of image quality, which will bring great trouble to the subsequent processing of the image. Image processing such as "edge detection", "image segmentation", "feature extraction" and "pattern recognition" has a serious impact. Therefore, image denoising is a very important part of image processing and the basis of subsequent image processing.

[0003] At present, there are many algorithms for image denoising, which are mainly divided into two categories: one is the traditional image denoising algorithm, and the more typical algorithm is Gaussian filtering. However, using this method for image denoising will cause blurring of...

Claims

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