Infrared image super-resolution reconstruction method based on structural transformation self-similarity

Abstract

The present invention discloses an infrared image super-resolution reconstruction method based on a structural transformation self-similarity. The method comprises the steps of calculating an appearance feature cost function and analyzing the structure information of an image block, calculating an area covariance cost function and analyzing image area structure information, calculating a dense residual cost function and reducing the redundancy of a reconstruction process, calculating a scale cost function and restraining loss information of a high-resolution image, and integrating and restraining appearance characteristics, a regional covariance, a dense residual error and scale estimation. According to the method, more image details can be retained, the amount of loss in the transmissionprocess of an image signal is reduced, and the transmission load is reduced. The resolution of the image can be effectively improved, the computational power consumption of a hardware algorithm can bereduced, the resource utilization of the image signal is improved, and the visual quality of the image is effectively improved.

Description

technical field [0001] The invention relates to a method for super-resolution reconstruction of infrared images based on structural transformation self-similarity, belonging to the technical field. Background technique [0002] Infrared image super-resolution reconstruction algorithm plays an important role in the analysis of infrared image scene understanding, and it is also an important branch of the field of machine vision understanding. The infrared image super-resolution reconstruction algorithm aims to improve the image resolution and retain more image detail information. [0003] Document 1 (J.Yang, J.Wright, T.Huang, Y.Ma. Image super-resolution as sparse representation of raw image patches [C], IEEE Computer Vision and Pattern Recognition, 2008: 1-8) and others proposed a A reconstruction method based on sparse coding, which uses sparse coding to analyze the structural information of image blocks and simulate the relationship between image blocks, so as to obtain c...

AI Technical Summary

Problems solved by technology

Although the above method can improve the resolution of natural images, it does not perform well in infrared image scenes, the details of infrared images are lost, and the quality of infrared images obtained is low.

This is mainly because traditional image super-resolution reconstruction algorithms are modeled for natural images, and few research works have focused on infrared image scenarios.

Images