Face super-resolution processing method and system based on closed link data reexpression

Abstract

The invention relates to a face super-resolution processing method and system based on the closed link data reexpression. The method comprises the following steps of firstly, constructing a training library comprising a high-resolution human face image library and a low-resolution human face image library corresponding to the high-resolution human face image library; secondly, dividing the low-resolution face image to be processed and the image in the training library into the image blocks with overlapped parts by adopting the same blocking mode; pre-training the global correlation degree of each block position in the low-resolution space and the high-resolution space; then performing data re-expression in the low-resolution image space on an input low-resolution image to be processed; performing the data expression from the low resolution space to the high resolution space on the input image to be processed after the low resolution space is reexpressed; carrying out data re-expressionof the high-resolution space; and finally, splicing the high-resolution face image blocks to obtain a high-resolution face image. The method and the system are particularly suitable for recovering the face image in a low-quality monitoring video.

Description

technical field [0001] The invention relates to the field of image processing and image restoration, in particular to a face super-resolution processing method and system based on closed-link data re-expression. Background technique [0002] The face super-resolution technology is to learn the corresponding relationship between high and low resolution of the input image by referring to the corresponding relationship between high and low resolution in the image library, and then according to the relationship between high and low resolution, it can estimate from the existing low resolution face image. The purpose of high-resolution face images. Face super-resolution is now widely used in many fields, one of the most representative fields is face image enhancement in surveillance video. With the widespread popularization of surveillance systems, surveillance video is playing an increasingly important role in the process of criminal evidence collection and criminal investigatio...

AI Technical Summary

Problems solved by technology

However, most of the existing methods only follow the traditional technical ideas, using a single projection method to simulate the quality improvement process from the low-resolution image library to the high-resolution image library, ignoring the difference between the quality degradation of the input image and the sample image. The difference in the degraded model leads to unsatisfactory reconstruction results, especially in real imaging environments such as surveillance, where the imaging environment cannot be strictly controlled and predicted, resulting in the presence of low-quality face images captured and those in the sample library. There is a large difference in the degradation process, and the reconstruction error caused by this difference cannot be simply processed with prior knowledge and model tools. Therefore, the effect of the face image restored by the traditional method is not satisfactory.

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