Research on Underwater Image Enhancement Technology Based on Generative Adversative Net work s

Geng-ren ZUO, Bo YIN, Xiao WANG, Ze-hua LAN


Under the influence of light refraction and particle scattering, the underwater image has a low contrast and color attenuation . This paper proposes an underwater image enhancement method based on Generative Adversative Net work s (GANs). The main principle is to apply the GANs to learn the pixel transformation between images through con frontation training, so as to achieve the purpose of image enhancement. In this paper, we select the real underwater image as the data set and degrade it before inputting the image into the neural network. Then we input the corresponding degenerate image , so that the neural network can learn the mapping from input image to real image and realize the color reduction and enhancement of underwater image. Experiments show that this method has strong robustness and can be applied to underwater shooting and under water navigation.


Underwater image enhancement, GANs, Confrontation training, Color restoration


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