Underwater Image Enhancement Based on Multi-Scale Attention and Contrast Learning

The two common degradations of underwater images are color distortion and blurred detail due to the absorption and dispersion of light by water. We propose an underwater image-enhancement algorithm model based on multi-scale attention and contrast learning to acquire underwater images with bright colors and clear details. The model adopts the encoding-decoding structure as the basic framework. To extract more fine-grained features, a multi-scale channel pixel attention module is designed in the encoder. The module uses three parallel branches to extract features at different levels in the image. In addition, the extracted features by the three branches are fused and introduced to the subsequent encoder and the corresponding decoding layer to improve the ability to extract network features and enhance details. Finally, a contrast-learning training network is introduced to improve the quality of enhanced images. Several experiments prove that the enhanced image by the proposed algorithm has vivid colors and complete detailed information. The average values of the peak signal-to-noise ratio and structural similarity index are up to 25. 46 and 0. 8946, respectively, and are increased by 4. 4% and 2. 8%, respectively, compared with the other methods. The average values of the underwater color image quality index and information entropy are 0. 5802 and 7. 6668, respectively, and are increased by at least 2% compared with the other methods. The number of feature matching points is increased by 24 compared to the original images.