MDSCN: multiscale depthwise separable convolutional network for underwater graphics restoration

Underwater imaging techniques have been a focus of research for computer vision. Underwater imaging frequently encounters challenges for poor image quality and slow restoration speed, thereby hindering human underwater exploration endeavors. To enhance the quality and improve the real-time performance of underwater image restoration, the paper proposes a lightweight underwater color image restoration network based on multiscale depthwise separable convolution. First, the algorithm tackles the problems of difficult convergence and slow training by improving the AdamW optimizer. Then, we propose a multiscale depthwise separable convolution module with RGB channel, which allows efficient extraction of image features based on the underwater light propagation properties. The MDSCN can effectively improve the processing speed and recovery effect of underwater images. Through experimentation and analysis, our algorithm outperforms traditional image processing methods and recent deep learning approaches in terms of visual effects and objective evaluation metrics. Furthermore, our algorithm also has a better performs than existing deep learning methods in processing speed, which demonstrates excellent generalizability and practicality. The research in the article is highly informative for the field of underwater computer vision. The dataset, training weights files and codes are publicly available https://gitee.com/raining-li/underwater-image-processing/tree/master.