Generative adversarial network training method, image completion method, equipment and storage medium

Abstract

The embodiment of the invention provides a generative adversarial network training method, an image completion method, equipment and a storage medium. In some exemplary embodiments of the present application, firstly, image completion training is performed by using a sample image including a missing region to obtain a preliminary completion network and a preliminary completion image; secondly, performing discriminator training by utilizing the preliminary completion image to obtain a first local context discriminator, a second local context discriminator and a global discriminator; and finally, performing confrontation training on the primary completion network through the combination of a first local context discriminator, a second local context discriminator and a global discriminator byusing the sample image containing the missing region to obtain an image completion network, wherein the first local context discriminator maintains the local consistency of image completion, the global context discriminator maintains the global consistency of image completion, and the second local context discriminator ensures the authenticity of texture information and the consistency of a completion center area and a surrounding area.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a generative adversarial network training method, an image completion method, a device, and a storage medium. Background technique [0002] Image completion techniques aim to synthesize missing or damaged regions in images, which is a fundamental problem in low-level vision. The technique has attracted widespread interest in computer vision and graphics because it can be used to fill in occluded image regions or repair damaged photos. In addition, before sharing a photo, users may need to make modifications to the image, such as erasing distracting scene elements, adjusting the position of objects in the image for better composition, or restoring image content in occluded image areas, etc. These and many other editing operations require automatic completion of missing regions in images, which has been an active research topic in the computer vision and grap...

AI Technical Summary

Problems solved by technology

Therefore, the existing results of image completion using GAN methods cannot maintain the consistency of image spatial structure and context information

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