INSPIRATION: A reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes

Hao Wang, Shixin Sun, Laibin Chang, Huanyu Li, Wenwen Zhang, Alejandro C. Frery, Peng Ren

Abstract: This paper delves into enhancing underwater images to improve human observation of underwater scenes. To this end, a reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes is proposed, referred to as INSPIRATION. This paradigm is so named because it constructs rewards for guiding model training by incorporating metrics inspired by human visual perception, eliminating the need for reference images. It overcomes the limitations of deep models, which often result in poor performance in dynamic and diverse underwater scenes due to the scarcity of underwater images with enhancement references. Specifically, a residual-enhancement network, composed of residual modules and a channel-attention module, is utilized to extract features as a state. An extensible collection encompassing diverse image enhancement algorithms is utilized to provide an image enhancement algorithm as an action. A multi-non-reference human visual perception metric increment is utilized as a reward, and proximal policy optimization (PPO) is utilized to conduct reinforcement learning. This paradigm treats underwater images as sole inputs during both training and implementation, learning and organizing a sequence of image enhancement algorithms to explicitly achieve a step-wise image enhancement process, aligning the enhanced images with human visual perception. Extensive qualitative and quantitative evaluations conclusively demonstrate that our paradigm outperforms nine state-of-the-art underwater image enhancement methods in terms of visual quality and achieves better performance on five underwater image quality assessment measures across three underwater image datasets. Additionally, the potential advantages of our paradigm as a pre-processing step in other underwater computer vision applications are demonstrated. The code has been released publicly for reproducibility and evaluations at https://gitee.com/wanghaoupc/uie_inspiration.