Total variational model non-blind restoration method and system

Abstract

The invention provides a total variational model non-blind restoration method and system. The method comprises the following steps: adopting a noise model to model the image degradation process; analyzing the homomorphic sub-blocks of the image, transforming the unknown parameter estimation of the noise model into the solution of linear equations; considering the sparsity of image gradient and maximum a posteriori estimation, and constructing a ROF total variational model according to the solution of unknown parameters; acquiring the luminance image, calculating the optimal solution of the ROFtotal variational model, and restoring the luminance image. This algorithm can suppress the noise and guarantee the image details and image quality at the same time. The method realizes non-blind restoration and reconstruction of biomedical images through automatic and intelligent means, and has the advantages of simple operation, high detection efficiency, high reliability and strong universality.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and system for non-blind restoration of a full variational model. Background technique [0002] Image inpainting is one of the key research contents in related fields such as computer image and computer vision. In 2000, Bertalmio and others proposed the famous BSCB model. The advantage of the algorithm is that it does not need to pre-estimate the pixel value of the unknown area, and maintains the diffusion along the isoluminance line direction of the edge, and has achieved better practical results to a certain extent. effect, but it has disadvantages such as complex algorithm implementation and slow execution speed. On this basis, Chan et al. proposed a total variation (TV) mathematical model and a curvature-based diffusion model (CDD) in 2002. The advantage is to sharpen image edges and retain edge information while removing image noise. Currently It ...

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Problems solved by technology

In 2000, Bertalmio and others proposed the famous BSCB model. The advantage of the algorithm is that it does not need to pre-estimate the pixel value of the unknown area, and maintains the diffusion along the isoluminance line direction of the edge, and has achieved better practical results to a certain extent. effect, but it has disadvantages such as complex algorithm implementation and slow execution speed

Compared with the previous class of algorithms, the image restoration algorithm for the general noise model can improve the image degradation problem caused by various noises to a certain extent, but there are still some key issues that need further research

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