  Underwater robots play an important role in oceanic geological exploration,
resource exploitation, ecological research, and other fields. However, the
visual perception of underwater robots is affected by various environmental
factors. The main challenge now is that images captured by underwater robots
are color-distorted. The hue of underwater images tends to be close to green
and blue. In addition, the contrast is low and the details are fuzzy. In this
paper, a new underwater image enhancement algorithm based on deep learning and
image formation model is proposed. Experimental results show that the
advantages of the proposed method are that it eliminates the influence of
underwater environmental factors, enriches the color, enhances details,
achieves higher scores in PSNR and SSIM metrics, and helps feature key-point
point matching get better results. Another significant advantage is that its
computation speed is much faster than other methods.
