PhySe-RPO: Physics and Semantics Guided Relative Policy Optimization for Diffusion-Based Surgical Smoke Removal
arXiv:2603.22844v1 Announce Type: new Abstract: Surgical smoke severely degrades intraoperative video quality, obscuring anatomical structures and limiting surgical perception. Existing learning-based desmoking approaches rely on scarce paired supervision and deterministic restoration pipelines, making it difficult to perform exploration or reinforcement-driven refinement under real surgical conditions. We propose PhySe-RPO, a diffusion restoration framework optimized through Physics- and Semantics-Guided Relative Policy Optimization. The core idea is to transform deterministic restoration into a stochastic policy, enabling trajectory-level exploration and critic-free updates via group-relative optimization. A physics-guided reward imposes illumination and color consistency, while a visual-concept semantic reward learned from CLIP-based surgical concepts promotes smoke-free and anatomically coherent restoration. Together with a reference-free perceptual constraint, PhySe-RPO produces
arXiv:2603.22844v1 Announce Type: new Abstract: Surgical smoke severely degrades intraoperative video quality, obscuring anatomical structures and limiting surgical perception. Existing learning-based desmoking approaches rely on scarce paired supervision and deterministic restoration pipelines, making it difficult to perform exploration or reinforcement-driven refinement under real surgical conditions. We propose PhySe-RPO, a diffusion restoration framework optimized through Physics- and Semantics-Guided Relative Policy Optimization. The core idea is to transform deterministic restoration into a stochastic policy, enabling trajectory-level exploration and critic-free updates via group-relative optimization. A physics-guided reward imposes illumination and color consistency, while a visual-concept semantic reward learned from CLIP-based surgical concepts promotes smoke-free and anatomically coherent restoration. Together with a reference-free perceptual constraint, PhySe-RPO produces results that are physically consistent, semantically faithful, and clinically interpretable across synthetic and real robotic surgical datasets, providing a principled route to robust diffusion-based restoration under limited paired supervision.
Executive Summary
The article introduces PhySe-RPO, a novel diffusion-based framework that addresses the critical issue of surgical smoke degrading intraoperative video quality. By transforming deterministic restoration into a stochastic policy and leveraging physics-guided and semantic rewards—derived from illumination consistency, color fidelity, and CLIP-based surgical concept recognition—the system enables exploration and adaptive refinement without paired supervision. This represents a significant shift from conventional deterministic pipelines toward a more flexible, reinforcement-inspired restoration model. The integration of both physics-based and semantic constraints ensures physical consistency and anatomical coherence, offering a robust alternative for diffusion-based desmoking in both synthetic and real surgical environments.
Key Points
- ▸ PhySe-RPO transforms restoration into a stochastic policy via relative optimization
- ▸ Physics-guided reward enforces illumination and color consistency
- ▸ Semantic reward via CLIP enhances anatomical coherence and smoke-free restoration
Merits
Innovative Framework
PhySe-RPO introduces a principled, policy-based approach to diffusion restoration, addressing a major gap in the lack of paired supervision and adaptability in current methods.
Semantic and Physics Alignment
The dual reward architecture—combining physics-based metrics with semantic CLIP-derived concepts—creates a more clinically interpretable and physiologically aligned restoration outcome.
Demerits
Dependency on Semantic Models
The reliance on CLIP-based semantic recognition may introduce variability due to model limitations in domain-specific surgical contexts or under occlusion.
Limited Validation Scope
While synthetic and real robotic datasets are used, broader clinical validation across diverse surgical specialties or real-time deployment scenarios remains unaddressed.
Expert Commentary
PhySe-RPO represents a thoughtful synthesis of physics-aware constraints and semantic intelligence within the diffusion restoration paradigm. The innovation lies not merely in the use of stochastic policies but in the intentional alignment of reward signals with clinically meaningful artifacts—illumination fidelity and anatomical coherence. This dual-reward architecture is particularly compelling because it bridges the gap between engineering-driven metrics (e.g., color consistency) and cognitive-driven expectations (e.g., anatomical plausibility). The authors’ ability to operationalize these signals via CLIP-based semantic embeddings without compromising interpretability is a major contribution. Moreover, the avoidance of critic-based updates through group-relative optimization reduces computational overhead and aligns with the practical demands of surgical real-time systems. This work sets a new benchmark for combining multi-modal constraints in medical imaging restoration, and its applicability extends beyond smoke removal to other noise-degraded modalities such as endoscopic or ultrasound imaging. One potential avenue for future work is the integration of real-time surgical telemetry data (e.g., instrument motion, tissue temperature) as additional reward channels to further enhance contextual awareness.
Recommendations
- ✓ Investigate the extension of PhySe-RPO to multi-modal inputs (e.g., video + instrument data) to enhance contextual awareness in real-time.
- ✓ Conduct comparative trials with conventional diffusion desmoking methods across diverse surgical specialties to quantify clinical impact and usability.
Sources
Original: arXiv - cs.AI
