Benchmarking Endoscopic Surgical Image Restoration and Beyond

TL;DR

This paper introduces SurgClean, a comprehensive open-source dataset for endoscopic image restoration, benchmarks 22 methods, and analyzes challenges in surgical scene enhancement to improve clinical outcomes.

Contribution

It provides a new dataset, benchmark, and insights into surgical image restoration, addressing a critical need for improved visual clarity in endoscopic procedures.

Findings

Significant performance gaps in current methods relative to clinical needs.

Structural and semantic differences between surgical and natural scenes.

Benchmarking results highlight areas for algorithm improvement.

Abstract

In endoscopic surgery, a clear and high-quality visual field is critical for surgeons to make accurate intraoperative decisions. However, persistent visual degradation, including smoke generated by energy devices, lens fogging from thermal gradients, and lens contamination due to blood or tissue fluid splashes during surgical procedures, severely impairs visual clarity. These degenerations can seriously hinder surgical workflow and pose risks to patient safety. To systematically investigate and address various forms of surgical scene degradation, we introduce a real- world open-source surgical image restoration dataset covering endoscopic environments, called SurgClean, which involves multi-type image restoration tasks from two medical sites, i.e., desmoking, defogging, and desplashing. SurgClean comprises 3,113 images with diverse degradation types and corresponding paired reference labels. Based on SurgClean, we establish a standardized evaluation benchmark and provide performance for 22 representative generic task-specific image restoration approaches, including 12 generic and 10 task-specific image restoration approaches. Experimental results reveal substantial performance gaps relative to clinical requirements, highlighting a critical opportunity for algorithm advancements in intelligent surgical restoration. Furthermore, we explore the degradation discrepancies between surgical and natural scenes from structural perception and semantic under- standing perspectives, providing fundamental insights for domain-specific image restoration research. Our work aims to empower restoration algorithms and improve the efficiency of clinical procedures.