A face image restoration method based on a generation antagonism network

Abstract

The invention discloses a face image restoration method based on a generation antagonism network. The method comprises the following steps: a face data set is preprocessed, and a face image with a specific size is obtained by face recognition of the collected image; In the training phase, the collected face images are used as dataset to train the generating network and discriminant network, aimingat obtaining more realistic images through the generating network. In order to solve the problems of instability of training and mode collapse in the network, the least square loss is used as the loss function of discriminant network. In the repairing phase, a special mask is automatically added to the original image to simulate the real missing area, and the masked face image is input into the optimized depth convolution to generate an antagonistic network. The relevant random parameters are obtained through context loss and two antagonistic losses, and the repairing information is obtainedthrough the generated network. The invention can not only solve the face image repairing with serious defective information, but also generate a face repairing image which is more consistent with visual cognition.

Description

technical field [0001] The invention belongs to the field of deep learning and image processing, and in particular relates to a face image restoration method based on a generative confrontation network. Background technique [0002] Image inpainting technology is an important branch in the field of image processing in recent years, which belongs to the interdisciplinary problems of pattern recognition, machine learning, statistics, computer vision and so on. Image restoration refers to the restoration and reconstruction of the missing image information caused by the image preservation process or the restoration after removing redundant objects in the image. Nowadays, researchers have proposed a variety of image restoration methods, which have been widely used in the fields of old photo restoration, cultural relic protection, and removal of redundant objects. [0003] Due to the inherent fuzziness and complexity of natural images, the traditional methods based on texture and...

AI Technical Summary

Problems solved by technology

[0005] There are two technical problems to be solved by the present invention. One is that the existing generative confrontation network has problems of unstable network training and mode collapse; the other is that the existing face repair images do not conform to visual cognition and the similarity is not high.

Aiming at these two problems, the present invention proposes a network design scheme that can not only solve the problems of unstable training and mode collapse of the generative confrontation network, but also generate and complement more natural and realistic face images

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