Can I read Degradation-aware and Color-corrected Network for Underwater Image Enhancement on EtoBox?

Degradation-aware and Color-corrected Network for Underwater Image Enhancement by Shibai Yin; Shuhao Hu; Yibin Wang; Weixing Wang; Chunxiao Li; Yee-Hong Yang is a Computer Science article available to read on EtoBox.

What is Degradation-aware and Color-corrected Network for Underwater Image Enhancement about?

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 branc

Who reads Degradation-aware and Color-corrected Network for Underwater Image Enhancement?

Author

Shibai Yin; Shuhao Hu; Yibin Wang; Weixing Wang; Chunxiao Li; Yee-Hong Yang

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)