Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image
  Enhancement
Jingxia Jiang Tian Ye Jinbin Bai Sixiang Chen Wenhao Chai Shi Jun Yun Liu Erkang Chen
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.