Five A\(^{+}\) Network: You Only Need 9K Parameters for Underwater Image Enhancement

• Published in: arXiv.org
• Main Authors: Jiang, Jingxia, Tian Ye, Bai, Jinbin, Chen, Sixiang, Chai, Wenhao, Shi, Jun, Liu, Yun, Chen, Erkang
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 15.05.2023
• Subjects: Image enhancement; Lightweight; Modules; Parameters; Real time; Source code; Underwater
• ISSN: 2331-8422

A lightweight underwater image enhancement network is of great significance for resource-constrained platforms, but balancing model size, computational efficiency, and enhancement performance has proven difficult for previous approaches. In this work, we propose the Five A\(^{+}\) Network (FA\(^{+}\)Net), a highly efficient and lightweight real-time underwater image enhancement network with only \(\sim\) 9k parameters and \(\sim\) 0.01s processing time. The FA\(^{+}\)Net employs a two-stage enhancement structure. The strong prior stage aims to decompose challenging underwater degradations into sub-problems, while the fine-grained stage incorporates multi-branch color enhancement module and pixel attention module to amplify the network's perception of details. To the best of our knowledge, FA\(^{+}\)Net is the only network with the capability of real-time enhancement of 1080P images. Thorough extensive experiments and comprehensive visual comparison, we show that FA\(^{+}\)Net outperforms previous approaches by obtaining state-of-the-art performance on multiple datasets while significantly reducing both parameter count and computational complexity. The code is open source at https://github.com/Owen718/FiveAPlus-Network.