Image processing method, system and device and medium

Abstract

According to the image processing method, system and device and the medium provided by the invention, an image processing mode is designed for the existing problems, and the mode can distinguish a high-definition image from a low-definition image from a real image; a degradation model is further designed, features of the low-definition images can be learned firstly, simulated low-definition imagesin one-to-one correspondence with the high-definition images are generated, and the problem that a super-resolution model is difficult to train on a real image is solved; meanwhile, a filter mechanism is further designed, the image is split into a high-frequency part and a low-frequency part, and the super-division effect of the real image is remarkably improved. Moreover, the method has universality, and according to different application scenes, the optimal super-resolution effect of the scene can be obtained only by using corresponding real images for training. The image after the image quality is improved through the super-resolution model has the advantages of no color distortion, clear details, sharp edges and good overall impression.

Description

Technical field[0001]The present invention relates to the technical field of image recognition, in particular to an image processing method, system, equipment and medium.Background technique[0002]Image quality is fundamental to the field of computer vision. Whether it is ordinary people's entertainment, target detection, target tracking, image segmentation, unmanned driving, etc., higher and higher requirements for image quality are put forward. However, the real images obtained by shooting equipment may have different contents of high-definition and low-definition images due to factors such as shooting equipment, shooting environment, shooting parameters, and shooting technology; for example, due to old shooting equipment, low-light environment, shooting distance, Factors such as video compression, low-quality images are inevitably produced; therefore, two identical low-definition and high-definition images cannot be found as training data. Therefore, it is necessary to improve the...

AI Technical Summary

Problems solved by technology

However, there is a large difference between artificial images and real images, and the super-resolution algorithm trained with artificial images is less effective in real images, usually manifested as a large amount of noise and unreasonable textures.

As a result, there is currently no way to use real images to train super-resolution models; at the same time, super-resolution models trained with artificial images cannot handle real images

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