Benchmarking Laparoscopic Surgical Image Restoration and Beyond
Journal:
arXiv
Published Date:
May 25, 2025
Abstract
In laparoscopic 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 impair 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 laparoscopic environments, called SurgClean, which
involves multi-type image restoration tasks, e.g., desmoking, defogging, and
desplashing. SurgClean comprises 1,020 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
understanding perspectives, providing fundamental insights for domain-specific
image restoration research. Our work aims to empower the capabilities of
restoration algorithms to increase surgical environments and improve the
efficiency of clinical procedures.
