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

Abstract

The present invention provides an image super-resolution reconstruction method based on the maximum posterior probability and non-local low-rank prior. The terminal and the readable storage medium adopt continuous image sequences as data input, and use the similarity between a single image and continuous images. As a priori knowledge, combined with the local grouping of image blocks, similar blocks are block-matched, and the spatial structure relationship at the image pixel level is mined; the maximum posterior probability framework is used to model, and the Gaussian distribution and the Gibbs distribution are used to fit the model parameters. Improve the generalization ability of the model; use low-rank truncation to suppress noise interference; use non-local low-rank constraints to regularize the image reconstruction process, and then use the local information in a single image and the local information between consecutive images to improve the quality of the target image. Alternately optimize the parameters in the model in each iteration to improve the robustness of the model and avoid local convergence. Finally, the reconstructed image blocks are weighted and averaged to obtain the 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...

AI Technical Summary

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.

Images