Degradation-aware and color-corrected network for underwater image enhancement

Abstract

Underwater image enhancement has played an important role in marine resource monitoring and preservation. However, the visual quality of underwater images is degraded due to light attenuation, background noise and water types, which brings challenging problems to underwater image enhancement. Existing Convolutional Neural Network (CNN) based methods do not fully explore degradation issues in different underwater scenes and also ignore color corrected priors, which limit their generalization and representation ability. To address the above mentioned problems, we propose a new Degradation-aware and Color-Corrected Network (DCN). Since it is difficult to estimate the degradation information of underwater image, we use Contrastive Learning (CL) to learn accurate degradation representation from input underwater images via a new degradation encoder. Then, to make the enhancement process adapt to different underwater images with the help of learned degradation representation and a color corrected prior, the DCN is built based on the proposed Degradation-aware and Color-corrected Block (DCB) for enhancing underwater images, which contains a degradation-aware branch, a color-corrected branch and an Edge Sharpness Layer (ESL). The degradation-aware branch recovers the clear image with the help of the degradation representation, while the color-corrected branch based on the color corrected prior compensates for the loss of information in the red channel. Furthermore, the ESL based on the unsharp masking principle is proposed and inserted at the end of the DCB for preserving details and sharpening edges. Such a design enables the DCN enhance underwater images, correct colors and sharpen edges simultaneously. The experiments on both synthetic and real images demonstrate that our method achieves pleasing visual results and outperforms state-of-the-art methods by a large margin.