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

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