Underwater image enhancement method based on multi-branch generation antagonistic network

Abstract

The invention discloses an underwater image enhancement method based on a multi-branch generation countermeasure network. The invention takes the underwater degraded original image, the underwater clear image after fusion processing under the same scene, and the underwater clear generated image under the same scene as a training sample set, and inputs the image to an attribute branch network and adiscrimination branch network to obtain an attribute map and a discrimination map. The GAN network weights are updated by the gradient descent of the cost function of the attribute graph and the discriminant graph respectively. Until the end of this network training, the model of underwater image enhancement is obtained. The key of the invention is to emulate the enhancement strategy of the underwater image which is degraded by different factors by using the characteristics of the generation against the network data driving and the strong imitation ability. A single model can be used to solvea variety of underwater image degradation problems caused by different reasons, and the model is more versatile. Attribute branching and discriminant branching are used to enhance the comprehensiveness and robustness of learning.

Description

technical field [0001] The invention belongs to the field of deep learning and relates to an underwater image enhancement method based on a multi-branch generation confrontation network. Background technique [0002] The complex underwater imaging environment and lighting conditions lead to quality degradation of underwater images. Traditional underwater image enhancement and restoration methods are problematic. The method based on the physical model has great limitations in the assumptions and prior knowledge it relies on. It is not adaptable to complex underwater environments, the designed underwater imaging mathematical model is inaccurate, and the model parameter estimation algorithm is complicated. The method of non-physical model ignores the optical properties of underwater imaging, which is easy to introduce color deviation, and the enhanced image is easy to produce oversaturated or undersaturated areas. Existing methods often have a good processing effect on a sing...

AI Technical Summary

Problems solved by technology

The method based on the physical model has great limitations in the assumptions and prior knowledge it relies on. It is not adaptable to complex underwater environments, the designed underwater imaging mathematical model is inaccurate, and the model parameter estimation algorithm is complex.

The method of non-physical model ignores the optical properties of underwater imaging, which is easy to introduce color deviation, and the enhanced image is easy to produce oversaturated or undersaturated areas

Existing methods often have a good processing effect on a single degradation phenomenon, but the overall comprehensiveness and robustness are not ideal, and there are great limitations in practical application

Images