Single-image super-resolution reconstruction method based on edge difference constraint

Abstract

Provided is a single-image super-resolution reconstruction method based on edge difference constraint. The method includes following three steps: step 1, extracting a texture principal direction characteristic of a training image through a Gabor filter, and performing a principal component analysis dictionary training to obtain a training dictionary; step 2, constructing a reconstruction model by employing the dictionary, and obtaining an initial reconstruction high-resolution image with a good edge structure through iterative threshold shrinkage; and step 3, describing an operator, a spatial distance, a pixel intensity, and edge orientation information by employing a histogram of oriented gradients between image blocks, establishing a non-local structure tensor optimization model, further optimizing and processing the initial reconstruction high-resolution image, and obtaining a final reconstruction high-resolution image with a substantial edge structure and abundant detail information. According to the method, by considering the difference between the initial reconstruction high-resolution image and an original clear image, the post-processing optimization method is further proposed, and the detail information of image edges and textures is abundant.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, and relates to a method for super-resolution reconstruction of a single image. Background technique [0002] With the rapid development of intelligent video image processing technology and network technology, high-resolution images are required in application fields such as video surveillance, satellite remote sensing imaging, military reconnaissance, medical imaging, and multimedia entertainment. In the process of acquiring visual images, due to the existence of system aberration, defocus, air disturbance and noise, the obtained images or sequences often have certain degradation or degradation, such as deformation, blurring, downsampling or noise. In order to obtain high-resolution images, the most direct and effective method is to increase the resolution level of hardware imaging equipment, that is, to increase the sensor element density per unit area. On the one hand, the un...

AI Technical Summary

Problems solved by technology

Adding image edge difference constraints in the image super-resolution reconstruction process can effectively solve the blurring and smoothing of details caused by the single image super-resolution reconstruction technology based on non-local self-similarity, thereby effectively maintaining the image while suppressing noise. The edge structure improves the quality of reconstructed high-resolution images

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