Image enhancement method and device and computer readable storage medium

Abstract

The invention provides an image enhancement method and device and a computer readable storage medium, and belongs to the technical field of image processing. The image enhancement method comprises the following steps: acquiring multiple groups of training data, wherein each group of training data comprises a first image and a second image; constructing an image enhancement model comprising a first-stage generative adversarial network and a second-stage generative adversarial network, training the image enhancement model by using multiple groups of training data, and training the first-stage generative adversarial network by using an enhanced low-frequency image generated based on the low-frequency features of the first image by taking the second image as a target image; taking the second image as a target image, generating an enhanced image based on the fused image of the first image, and training the second-stage generative adversarial network; the fused image is obtained by fusing the first image and the enhanced low-frequency image; and inputting a third image to be enhanced into the trained image enhancement model, and outputting a fourth image after image enhancement. The image quality of the image can be improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, and in particular, to an image enhancement method, device and computer-readable storage medium. Background technique [0002] Panoramic images captured by panoramic cameras usually have a field of view of 180 degrees or higher, however, compared to flat images captured by high-quality cameras (such as digital SLR cameras or digital single-lens reflex cameras), panoramic images have a range of sharpness, resolution, and color. The difference and so on are relatively poor. [0003] In order to solve the above problems, the prior art proposes methods for image enhancement and Super-Resolution (SR) processing. These methods have all aspects of image sharpening, denoising, deblurring, contrast improvement and chromatic aberration correction. Good improvements have been made to improve image quality. However, the above methods usually do not recover well in terms of image details, wh...

AI Technical Summary

Problems solved by technology

However, the above methods usually do not recover well in terms of image details, while also producing some artifacts

Images