Face super resolution rebuilding method based on principal component sparse expression

Abstract

A face super resolution rebuilding method based on principal component sparse expression comprises the following steps: enabling an input low resolution facial image, an input low resolution facial sample image and an input high resolution facial sample image to be respectively divided into image blocks which are mutually overlapped, conducting principal component decomposition for each position image block of the images, obtaining a principal component expression base, conducting sparse restraining projection for each image block of the input low resolution facial image according to the corresponding principal component expression base of an image block in a sample database, converting an obtained principal component sparse expression coefficient into a sample expression space, replacing each position block of the low resolution facial image by the corresponding position block of the high resolution facial image, combining and joining the image blocks with high resolution together, and obtaining an output high resolution image. According to the face super resolution rebuilding method based on the principal component sparse expression, the principal component sparse expression of the position blocks is provided, inner information and noise information of the input image blocks are distinguished, expression accuracy of the image blocks under noise environment is improved, and impersonal image quality of the high resolution rebuilding image is improved.

Description

technical field [0001] The invention relates to the field of super-resolution of human face images, in particular to a noise-robust human face super-resolution reconstruction method based on principal component sparse expression. Background technique [0002] In recent years, video surveillance systems have been widely used in urban security work. However, in many application scenarios, because the camera is far away from the target of interest, the imaging pixels of the target in the surveillance video are usually small, lacking enough detailed information, and unable to meet the recognition requirements. Especially in criminal investigation applications, the imaging resolution of the target face of interest is too low to meet the needs of human eye recognition, which makes it difficult to effectively lock evidence. Therefore, performing super-resolution enhancement on low-resolution face images in low-quality surveillance videos to obtain more local detail information for...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a face image super-resolution reconstruction method based on principal component sparse expression, which solves the problem that noise and intrinsic features cannot be distinguished in existing similar block-based expression algorithms. Adapting to select intrinsic features of images for expression, improving the quality of synthesized high-resolution face images

Images