AquaTree: Deep Reinforcement Learning-Driven Monte Carlo Tree Search for Underwater Image Enhancement

Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a multimodal state representation combining CIELab color histograms with deep perceptual features, (2) a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency, and (3) an alternating training strategy co-optimizing enhancement sequences and network parameters. We further propose two inference schemes: an MCTS-based approach prioritizing accuracy at higher computational cost, and an efficient network policy enabling real-time processing with minimal quality loss. Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority, significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.