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Open Access

ARTICLE

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

Chao Li1,3,#, Jianing Wang1,3,#, Caichang Ding2,*, Zhiwei Ye1,3
1 School of Computer Science, Hubei University of Technology, Wuhan, 430068, China
2 School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China
3 Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, School of Computer Science, Hubei University of Technology, Wuhan, 430068, China
* Corresponding Author: Caichang Ding. Email: email
# These authors contributed equally to this work

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071242

Received 03 August 2025; Accepted 23 October 2025; Published online 10 December 2025

Abstract

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.

Keywords

Underwater image enhancement (UIE); Monte Carlo tree search (MCTS); deep reinforcement learning (DRL); Markov decision process (MDP)

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