BeTAR: Belief-Guided Trajectory Learning with Agentic Reinforcement Learning for Multi-Degradation Surgical Image Restoration

Surgical images are frequently affected by multiple coupled degradations, including smoke, low illumination, specular highlights, blur, water droplets, and low resolution, making fixed restoration pipelines difficult to generalize across complex clinical scenes. To address this challenge, we propose BeTAR, a degradation-aware agentic framework that integrates structured degradation perception with belief-guided restoration planning for multi-degradation surgical image restoration. First, a vision-language model is trained to identify degradation types and severity levels and organize them into an interpretable belief state, providing structured environmental information for subsequent decision making. Second, conditioned on this belief state, we formulate restoration planning under a Partially Observable Markov Decision Process (POMDP) framework and optimize an Agentic-RL policy to generate complete open-loop restoration trajectories, allowing the agent to jointly determine tool selection, execution order, and termination without relying on a predefined restoration chain. The policy is further optimized using a reference-free trajectory-level reward that combines no-reference image quality assessment with tool-use constraints, enabling training without paired clean images. Experiments on both in-distribution and independent surgical image test sets show that BeTAR achieves an F1 score of 0.908 for degradation recognition and ranks first on five of six restoration metrics on both test sets.