Face image super-resolution reconstruction method based on discriminable attribute constraint generative adversarial network

Abstract

The invention discloses a face image super-resolution reconstruction method based on a discriminable attribute constraint generative adversarial network, and belongs to the field of digital images / video signal processing. The method comprises the following steps: firstly, designing a processing flow of face detailed information enhancement; secondly, designing a network structure according to theflow, and acquiring an HR image from an LR image through the network; and lastly, performing face verification accuracy evaluation on the HR image through a face recognition network. Through adoptionof the method, enhancement including LR face image detailed information can be completed, and the accuracy of face verification is increased. Secondly, the generative network completes compensation ofimage high-frequency information firstly, then completes image amplification by subpixel convolution, and finally completes stepwise image amplification through a cascade structure, thereby completing enhancement of image detailed information. An attribute constraint module are trained together with a perception module and an adversarial model in order to perform fine adjustment of the performance of a network reconstructed image. Finally, a reconstructed image of the generative network is input into a face verification network, so that the accuracy of face verification is increased.

Description

technical field

[0001] The invention belongs to the field of digital image / video signal processing, and in particular relates to a human face image super-resolution reconstruction method based on a discriminable attribute constraint generation confrontation network. Background technique

[0002] With the rapid development and wide application of multimedia technology, high-quality images and videos have more and more application value. In video surveillance, human face is one of the most important objects. However, affected by various factors such as collection distance, ambient light, and compression distortion, face images in applications such as video surveillance are often blurred, low-resolution, and low-quality images, which seriously affect subsequent intelligent face analysis technologies. Applications. Existing methods mostly use image super-resolution restoration methods based on deep learning to improve the image quality of low-resolution images. However, these ...

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