Underwater Image Restoration Based on An Improved Underwater Image Formation Model

Underwater images often show characteristics of low contrast, low visibility and color distortion. To improve underwater image quality, a novel underwater image restoration algorithm is proposed in this paper. First of all, we put forward an underwater image restoration network (UIRnet) based on CNN(Convolutional Neural Network) for transmission estimation in the underwater imaging model. Besides, the Leaky ReLU(Rectified Linear Units) activation function and SENet(Squeeze-and-Excitation Networks) unit are used to improve the ability of extracting image features and training parameters. Secondly, the fuzzy patch dataset which is used as input dataset is synthesized by the underwater optical imaging model and randomly generated transmission. Meanwhile, the corresponding transmission value is used as label dataset. Then, we use the dataset to train and optimize all parameters in the UIRnet. Finally, the trained UIRnet is used in underwater image restoration. The experimental results show that the proposed algorithm can restore the details of the image, improve the contrast, and have a more natural visual effect.