Toward Zero-Shot Learning for Visual Dehazing of Urological Surgical Robots

TL;DR

This paper introduces an unsupervised zero-shot dehazing method, RSF-Dehaze, for improving visual clarity in urological robotic surgery, supported by a new dataset and extensive experiments.

Contribution

The paper presents the first publicly available dehaze dataset for urological robotic surgery and proposes a novel zero-shot dehazing method with a region similarity filling module.

Findings

RSF-Dehaze outperforms 20 classical and advanced algorithms.

The method significantly improves tissue recovery in blurred regions.

The dataset covers three common urological surgical scenarios.

Abstract

Robot-assisted surgery has profoundly influenced current forms of minimally invasive surgery. However, in transurethral suburethral urological surgical robots, they need to work in a liquid environment. This causes vaporization of the liquid when shearing and heating is performed, resulting in bubble atomization that affects the visual perception of the robot. This can lead to the need for uninterrupted pauses in the surgical procedure, which makes the surgery take longer. To address the atomization characteristics of liquids under urological surgical robotic vision, we propose an unsupervised zero-shot dehaze method (RSF-Dehaze) for urological surgical robotic vision. Specifically, the proposed Region Similarity Filling Module (RSFM) of RSF-Dehaze significantly improves the recovery of blurred region tissues. In addition, we organize and propose a dehaze dataset for robotic vision in urological surgery (USRobot-Dehaze dataset). In particular, this dataset contains the three most common urological surgical robot operation scenarios. To the best of our knowledge, we are the first to organize and propose a publicly available dehaze dataset for urological surgical robot vision. The proposed RSF-Dehaze proves the effectiveness of our method in three urological surgical robot operation scenarios with extensive comparative experiments with 20 most classical and advanced dehazing and image recovery algorithms. The proposed source code and dataset are available at https://github.com/wurenkai/RSF-Dehaze .