Communications in Information and Systems

Volume 23 (2023)

Number 1

CATDS: cross aggregation transformer-based dynamic supplement network for underwater image enhancement

Pages: 1 – 30

DOI: https://dx.doi.org/10.4310/CIS.2023.v23.n1.a1

Authors

Zhixiong Huang (School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China)

Jinjiang Li (School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China)

Zhen Hua (School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China)

Linwei Fan (School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China)

Abstract

The complex underwater environment causes light to suffer from scattering effects and wavelength-dependent attenuation, and underwater images exhibit color deviation and low contrast, which hinder the progress of related underwater tasks. Deep learning algorithms now make extensive use of multi-scale features to improve underwater image quality, but the majority of these methods do not take channel differences into account while propagating features. To this end, we propose a cross aggregation transformer (CAT), which utilizes three stages of projection-crossing aggregation to adaptively select beneficial channels. This paper also designs a dynamic supplement underwater image enhancement network, which consists of a shallow network and an enhancement network. Through the encoder/decoder structure, the enhancement network restores the original appearance of the underwater image, while the shallow network extracts the shallow features at different scales. Both networks are designed to focus on under-enhanced regions and supplementary details in real time through the residual supplement module (RSM). The experimental findings demonstrate that CAT and RSM efficiently improve network performance and elevate the network above other advanced methods on various datasets.

Full Text (PDF format)

Jinjiang Li’s research was supported in part by the National Natural Science Foundation of China (61772319, 62002200, 62202268 and 62272281), and by the Shandong Natural Science Foundation of China (ZR2021QF134, ZR2021MF107).

Received 3 January 2022

Published 17 April 2023