Anisotropism wavelet image processing method based on thermonuclear pyramid

Abstract

The invention provides an anisotropism wavelet image processing method based on a thermonuclear pyramid. According to the method, at the theoretical part, wavelets are obtained through thermonuclear difference between adjacent layers of an image pyramid, and are equal to negative one order derivation of a thermal diffusion partial differential equation, relative to time; at an implementation part that: 1), mapping images to be a weight undirected graph, coding structural characteristics into a Laplacian matrix, and achieving anisotropism thermal diffusion; 2), dividing the images into image subblocks with overlaps, calculating wavelets of all image blocks in a parallel mode, reducing calculated amount by using a block overlapping method and effectively eliminating blocking effects after recombination; 3), adopting Krylov subspace technology to accelerate image block wavelet transform calculation, and avoiding time-consuming matrix spectral factorization. At the application part, the method is applied to image processing with structural protecting function. The data related anisotropism wavelet system provided by the invention achieves structure protection multiscale decomposition for the images, and shows excellent performance in various image processing applications.

Description

technical field [0001] The invention relates to an anisotropic wavelet image processing method based on a thermonuclear pyramid. Background technique [0002] In the field of computer graphics and computer vision, edge protection filters have been widely concerned and studied, and are used in a variety of computer image processing applications, such as: edge protection smoothing, detail enhancement, image coloring, high dynamic range image compression, image style etc. In recent decades, various structure-aware algorithms have been proposed and have achieved excellent results. The research goal of this direction is to effectively protect the inherent structure of the image while smoothing unimportant details. [0003] Structure-aware filters can be divided into two categories: local filters and global filters. The local filter usually takes a certain pixel as the center, and calculates the similarity between all the pixels in the surrounding neighborhood and the central pi...

AI Technical Summary

Problems solved by technology

The advantage of this type of filter is that it can protect high-contrast structural information well, but it cannot effectively process local details, and the filtered effect is too smooth

[0004] At present, the problems faced by the structure-aware filter are: how to effectively extract the structural information in the image, how to effectively deal with the salient edges during multi-scale decomposition, and how to compress the halo phenomenon around the boundary and eliminate the gradient flip phenomenon

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