An image processing method, system, device and medium

Abstract

An image processing method, system, device and medium provided by the present invention, aiming at the current problems, designs an image processing method, which can distinguish high-definition images and low-definition images from real images; and also designs a The degradation model can first learn the characteristics of the low-definition image, and generate a simulated low-definition image corresponding to the high-definition image one by one, which solves the problem that the super-resolution model is difficult to train on the real image; at the same time, the present invention also designs a filter mechanism, Splitting the image into high-frequency and low-frequency parts significantly improves the super-resolution effect of real images. Moreover, the present invention has versatility, and according to different application scenarios, the optimal super-resolution effect of the scenario can be obtained only by using corresponding real images for training. Moreover, the image after the image quality is improved by the super-resolution model in the present invention has no distortion in color, clear details, sharp edges, and a good overall look and feel.

Description

technical field [0001] The present invention relates to the technical field of image recognition, in particular to an image processing method, system, device and medium. Background technique [0002] Image quality is fundamental to the field of computer vision. Whether it is ordinary people's film and television entertainment, or target detection, target tracking, image segmentation, unmanned driving, etc., there are higher and higher requirements for image quality. However, the real images obtained through the shooting equipment may have different content between 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, Due to factors such as video compression, low-quality images are inevitably generated; thus, it is impossible to find two identical low-definition and high-definition images as t...

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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