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

arXiv: https://arxiv.org/abs/2410.01395 · HTML: https://mlsanity.com/paper/2410.01395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks](https://mlsanity.com/paper/1810.03982/) (2018)
- ["Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors](https://mlsanity.com/paper/1812.00467/) (2018)
- [Gated Context Aggregation Network for Image Dehazing and Deraining](https://mlsanity.com/paper/1811.08747/) (2018)
- [Restoring Images in Adverse Weather Conditions via Histogram Transformer](https://mlsanity.com/paper/2407.10172/) (2024)
- [Zero-Shot Noise2Noise: Efficient Image Denoising without any Data](https://mlsanity.com/paper/2303.11253/) (2023)
- [You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network](https://mlsanity.com/paper/2006.16829/) (2020)
- [UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2205.01871/) (2022)
- [A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution](https://mlsanity.com/paper/2404.15620/) (2024)

## Built on by (most cited first, 2 of 2)

- [TTTFusion: A Test-Time Training-Based Strategy for Multimodal Medical Image Fusion in Surgical Robots](https://mlsanity.com/paper/2504.20362/) (2025)
- [Dehaze-GaussianImage: Zero-Shot Dehazing via Efficient 2D Gaussian Splatting Representation](https://mlsanity.com/paper/2606.16163/) (2026)

## Abstract (arXiv)

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 .