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 ∼ 9k parameters and ∼ 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.
Cite
@article{arxiv.2305.08824,
title = {Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement},
author = {Jingxia Jiang and Tian Ye and Jinbin Bai and Sixiang Chen and Wenhao Chai and Shi Jun and Yun Liu and Erkang Chen},
journal= {arXiv preprint arXiv:2305.08824},
year = {2023}
}