Training method and device of image restoration model, image restoration method and device and equipment

Abstract

The embodiment of the invention discloses a training method and device of an image restoration model, an image restoration method and device and equipment. The method comprises the steps: carrying out the image feature extraction of a real first pixel quality image and an artificially synthesized first pixel quality image, and obtaining a regenerated real first pixel quality image, a pseudo real first pixel quality image and a reconstructed second pixel quality image through a real first pixel quality image generator and a real second pixel quality image generator; and respectively calculating domain alignment loss, image generation loss and image reconstruction loss, and performing training by using the obtained loss. The image restoration model obtained after training has better generalization ability, image restoration is more accurate, and the model performance is better. The to-be-restored first pixel quality image is restored by using the trained image feature encoder and the real second pixel quality image generator, so that a second pixel quality image with a relatively good restoration effect can be obtained, and the image use requirements are met.

Description

technical field [0001] The present application relates to the field of image processing, in particular to an image restoration model training method, device and equipment, and an image restoration method, device and equipment. Background technique [0002] In the process of image generation and processing, it may be affected by the equipment, resulting in low pixel quality of the image, which cannot meet the needs of image use. In order to improve the pixel quality of an image with lower pixel quality, image super-resolution reconstruction technology can be used to process the image with lower pixel quality, improve the pixel quality of the image with lower pixel quality, and obtain an image with higher pixel quality. [0003] At present, the image super-resolution reconstruction technology can be realized through the image restoration model constructed by the convolutional neural network. The image inpainting model needs to be generated through training image training. Th...

AI Technical Summary

Problems solved by technology

However, the quality of artificially constructed low-pixel-quality images is low, resulting in poor performance of the trained image inpainting model, which cannot meet the needs of image pixel quality inpainting.

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