Face image repairing method based on generative adversarial network

Abstract

The invention discloses a face image repairing method based on a generative adversarial network. The method comprises the following steps that: (1) searching a great quantity of images which contain complete and clear faces, and establishing a face image database; (2) constructing the generative adversarial network; (3) training the generative adversarial network, and optimizing the parameters ofa generator and a descriminator in the generative adversarial network; and (4) inputting random vectors which obey normal distribution into the trained generator, generating the face image, comparingthe intact area of the face image to be repaired with the corresponding area of the generated image, continuously regulating the input vectors until the intact area of the face image to be repaired and the corresponding area of the generated image are similar, and finally, replacing the pixel value of a blocked or damaged area in the face image to be repaired by the pixel value of the corresponding area for generating the face image. By use of the method, the generative adversarial network with a deep learning structure is adopted by aiming at the problem that the blocked or damaged face imageis repaired, and therefore, an image repairing problem in image processing is effectively solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a face image restoration method based on a generative confrontation network. Background technique [0002] With the popularity of electronic photographic equipment, digital photos have entered every aspect of people's lives, and image processing has therefore received extensive attention. Image inpainting is an extremely important part of image processing. Image restoration uses the information of the intact part of the image to fill in the occluded, damaged or redundant parts. It can be used to remove the occlusion of photos, repair damaged cultural relic images, image data preprocessing and other fields. [0003] Traditional image inpainting techniques often require specific shapes of defect parts and simple texture repetition, which limits the application scenarios of image inpainting. With the improvement of computer computing power and the maturity of...

AI Technical Summary

Problems solved by technology

[0003] Traditional image restoration techniques often require specific shapes of defect parts and simple texture repetition, which limits the application scenarios of image restoration

Images