An underwater image enhancement algorithm based on conditional generation adversarial networks（CGAN） is proposed to solve the problem of unclear underwater image and color distortion caused by light propagation in water.In order to achieve better enhancement effect

fully matched input and output images are used for model training to obtain enhanced images end-to-end.In generated network model

U-Net network structure is adopted to reduce network information load.Meanwhile

in order to capture more low-frequency features

L

1

loss function is introduced to make the generated results more real and clear.Experimental results show that the training model can effectively solve the problem of color distortion and blur in the underwater image

and has a good enhance

ment effect on the underwater image.