Method for denoising and enhancing anisotropic diffusion image with controllable diffusion degree

Abstract

The invention discloses a method for denoising and enhancing an anisotropic diffusion image with controllable diffusion degree based on a track model. The method is characterized by comprising the following steps of: firstly, calculating a structure tensor and a Hessian matrix of each pixel point; secondly, decomposing the characteristic values of the structure tensor of each image point; thirdly, constructing a diffusion tensor of each image point, enabling the characteristic vector of the diffusion tensor to be the characteristic vector of the structure tensor, making the characteristic value as a function of the diffusion rate, i.e., a function of the characteristic value of the structure vector, and making the diffusion degree controllable by adjusting the parameter of the function; and finally, iteratively solving the track model. By adopting the method, the image can be effectively denoised and enhanced.

Description

technical field [0001] The invention relates to image processing technology, especially image denoising technology, which can be used to process images with additive or multiplicative noise. Background technique [0002] Image denoising, especially for filtering algorithms for suppressing speckle noise, usually uses Lee filtering, Kuan filtering, Frost filtering and other noise reduction methods. What these methods have in common is to select an appropriate window size and adjust The filter function performs filtering, but the window selection criteria and the filters adopted by different methods are different. But the common weakness is that these methods are isotropic filtering in the edge direction, which blurs the image structure and detail information. Later, a series of filtering methods based on anisotropic diffusion were developed, one of which was Tschumperle's article "Vector-Valued ImageRegularization with PDE's: A Common Framework for Different Applications" (pu...

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Problems solved by technology

Nevertheless, the filter generated by this diffusion rate function still has shortcomings: it lacks controllability and relaxation, that is, for any noisy image, regardless of the noise level, the diffusion rate function is only related to the average curvature; and it does not have The ability to simultaneously enhance features such as edges

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