High-resolution image reconstruction method based on directivity gradient

Abstract

The invention discloses a high-resolution image reconstruction method based on a directivity gradient. The high-resolution image reconstruction method comprises the following steps: 1) obtaining a main texture direction [Theta] for inputting a low-resolution image I belongs to R<m*n>; 2) utilizing a bicubic interpolation method to interpolate the low-resolution image I into a high-dimensionality interpolation image IL; 3) carrying out variable initialization and parameter selection; 4) obtaining a fuzzy operator matrix Fh and a conjugate fuzzy operator matrix FHh; 5) updating the value of xij; 6) updating the value of IH; 7 updating the value of Xij; and 8) judging whether convergence is carried out. A regular item of the directivity gradient is introduced to better show and dig direction texture information implied in the image so as to introduce prior information with better value into an ill-posed high-resolution reconstruction problem, and the high-resolution image with a better effect is obtained.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a method for reconstructing a high-resolution image from a low-resolution image by using a directional gradient and a convex optimization algorithm. Background technique [0002] Reconstructing a high-resolution image from a single low-resolution image has always been an important topic in the field of computer vision and image processing, and it is also a very challenging topic. [0003] The usual technical means can be divided into three categories: interpolation methods, machine learning methods, and optimization reconstruction methods based on sparse constraints. The implementation of the interpolation method is relatively simple, but usually the obtained high-resolution image is very blurred. Machine learning methods learn a large number of low-resolution and high-resolution image features to obtain corresponding correspondences to guide the reconstruction of high-resolution i...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that there is no deep mining of directional texture information in the existing high-resolution image reconstruction technology, and propose a high-resolution image reconstruction method based on directional gradients, which can achieve deeper Mining the useful information hidden in the image as prior knowledge makes the reconstructed image higher resolution

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