Human face super-resolution method based on residual keeping

Abstract

The invention discloses a human face super-resolution method based on residual keeping. According to the human face super-resolution method, based on a learning method, high and low resolution training image pairs are partitioned uniformly first, and processing is conducted in a block set based on positions: the smooth high frequency component and the standard deviation high frequency component are estimated on the corresponding positions of the training block set for all blocks of an input image, and the high-frequency compensation component is estimated through a residual training set. The linear combination of the three high-frequency information is used for reconstructing a high-resolution image on a high-resolution average face, and therefore the noise problem in super-resolution restoration of a human face image (like a monitoring image) with serious noisy points can be solved.

Description

technical field [0001] The invention belongs to the field of image processing (image restoration), aims at the demand for face image restoration in low-quality surveillance video, and specifically relates to a face super-resolution method based on residual error preservation. Background technique [0002] In recent years, with the rapid development of security monitoring systems, monitoring and forensics has played an increasingly important role in the fields of security prevention and crime forensics, among which face image forensics is one of the important concerns of monitoring and forensics. However, due to the serious blur and noise caused by the long distance between the camera and the target face, bad weather (rain, fog, etc.), poor lighting conditions, etc. in the surveillance video, the usable pixels of the face image captured in the surveillance video are extremely low, and the image recovery , identification is often severely hindered. Therefore, in order to redu...

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Problems solved by technology

[0004] The face super-resolution problem is a problem with infinite solutions, because a low-quality image may correspond to many different high-quality images

[0006] However, most of the existing learning-based classical methods only learn the statistical relationship between the high and low resolution of the fixed face database according to the traditional technical ideas, and improve the super-resolution recovery effect by uniformly learning the information of each frequency band of the image; In the process, the high-frequency details have not been well utilized and paid attention to

This type of method can get good results in dealing with general face super-resolution problems, but when the image quality is very low, the pixels will be severely damaged and aliased, and the effect is not satisfactory.

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