Non-negative matrix factorization-based face super-resolution processing method

Abstract

The invention relates to the technical field of image super-resolution processing, in particular to a non-negative matrix factorization-based face super-resolution processing method. The method comprises the following steps: performing face alignment on high-resolution face images in a sample library, reading the aligned sample image library, utilizing a non-negative matrix factorization algorithm to perform a factorization operation to obtain a basic image W, performing alignment on input low-resolution face images to obtain the non-negative matrix factorization expression coefficient e of a target high-resolution face image, obtaining the target high-resolution image Z1=We in combination with the basic image W and the expression coefficient e and dividing the important areas of the face images in the sample library; performing factorization synthesis on the divided local areas; and weighting and combining the synthesized local area and the image Z1 to obtain a super-resolution image Z2. The method has the advantages of increasing semantic constraint like that the grayscale of the image is non-negative, improving the expression capacity of the characteristic basic image and finally improving the quality of the super-resolution image.

Description

technical field [0001] The invention relates to the technical field of image resolution processing, in particular to a face super-resolution processing method based on non-negative matrix decomposition. Background technique [0002] In most surveillance scenarios, there is a long distance between the camera and the objects of interest in the scene, which often results in low resolution of these objects, and human faces in surveillance videos are one of the most common objects of interest. Because low-resolution face images lose a lot of facial feature details, faces are often difficult to identify, and the resolution of face images has become an important factor restricting the performance of face recognition and subjective recognition applications, effectively enhancing face images Resolution becomes a burning issue. [0003] In recent years, many super-resolution techniques have been proposed. Most super-resolution algorithms try to generate a super-resolution image from ...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a face super-resolution processing method based on non-negative matrix decomposition, to solve the traditional PCA-based method without considering the local structural characteristics, feature expression ability is not strong, difficult to semantic interpretation and other problems

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