Image super-resolution reconstruction method based on maximum posteriori probability and non-local low-rank prior, terminal and readable storage medium

Abstract

The invention provides an image super-resolution reconstruction method based on maximum posteriori probability and non-local low-rank prior. The method comprises the steps that a continuous image sequence is adopted as data input, the similarity between a single image and a continuous image is used as priori knowledge, block matching is conducted on similar blocks through an image block local grouping mode, and the spatial structure relation of the image pixel level is mined; modeling is performed with a maximum posterior probability framework, Gaussian distribution and Gibbs distribution areused for fitting model parameters, and the generalization ability of the model is improved; noise interference is suppressed in a low-rank truncation mode; a non-local low-rank constraint regularization image reconstruction process is adopted, and local information in a single image and local information between continuous images are utilized to improve the quality of a target image. Parameters inthe model are alternately optimized in each iteration, the robustness of the model is improved, and local convergence is avoided. Finally, weighted averaging is performed on the reconstructed image blocks to obtain a target high-resolution image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image super-resolution reconstruction method based on maximum a posteriori probability and non-local low-rank prior, a terminal and a readable storage medium. Background technique [0002] Vision is one of the main ways for humans to obtain information from the outside world, and the performance of most vision-based applications depends on the quality of images. High Resolution (HR) images have high resolution and contain rich image details and more image information, so they are of great value in practical applications such as medical fields, video surveillance fields, and remote sensing fields. Although in these practical application fields, image imaging technology has become mature, but due to the mutual constraints of imaging equipment, imaging environment, human interference and other factors, the resolution of most images is very low. For example, in the medic...

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Problems solved by technology

However, the calculation complexity of the method is high, requiring multiple iterations and projections, the convergence speed is relatively slow, and the algorithm stability is not high.

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