Underwater image restoration with multi-scale shallow feature extraction and detail enhancement network

Abstract

Underwater imaging and image processing techniques have recently attracted increasing attention in underwater target detection, archaeology, resource exploration, etc. However, the captured underwater images usually suffer from colour deviation (blue, green, yellow, etc.) and detail blurring problems due to the light absorption, light scattering effects, noises and target occlusions in the water. To solve the above two degradation problems, we propose an underwater image restoration method with multi-scale shallow feature extraction and detail enhancement network. Specifically, we develop a multi-scale shallow feature extraction module to address local detail blurring and alleviate colour deviation issues. Additionally, we design a local detail enhancement module based on an encoder-decoder structure. Quantitative and qualitative experiment results indicate that the proposed method outperforms recently reported state-of-the-art deep-learning-based underwater image restoration methods on four public underwater image datasets. Besides, we implement many ablation experiments to validate the effectiveness of each module in the proposed network.