Training method of weak light image enhancement model and weak light image enhancement method

Abstract

The invention provides a training method of a weak light image enhancement model and a weak light image enhancement method, and the training method of the weak light image enhancement model comprises the steps: inputting a normal light image and a weak light image into an initial decomposition module, and obtaining a normal light reflection image, a normal light illumination image, a weak light reflection image and a weak light illumination image; inputting the weak light reflection image and the weak light illumination image into an initial color recovery module to obtain a color recovery image; determining a first loss function value, a second loss function value and a third loss function value; training an initial decomposition module according to the first loss function value and the second loss function value, and training an initial color recovery module according to the third loss function value. According to the invention, the trained decomposition module and color recovery module can obtain a better output result, the detail features and color information of the image are gradually recovered, and the weak light enhanced image generated by the weak light image enhancement model obtained through training can obtain a better result in image detail and color recovery.

Description

technical field [0001] The present application relates to the field of image processing, in particular to a training method for a low-light image enhancement model and a low-light image enhancement method. Background technique [0002] Images taken under low-light or low-light conditions have defects such as low brightness and low contrast. In addition, a lot of noise is often hidden in low-light areas of low-light images, and there are problems such as color distortion. On the other hand, high-quality The image is a necessary condition for image recognition, analysis and understanding, therefore, low-light image enhancement is an important step to improve image quality and prepare for subsequent image processing. [0003] Existing low-light image enhancement methods, such as histogram equalization methods, Retinex model-based methods, and image defogging methods, cannot effectively restore image detail information, and are prone to local under-enhancement, or local over-enh...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that the existing low-light image enhancement methods have the problems of color distortion and insufficient detail enhancement

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