PyTorch implementation of Self-Supervised Video Desmoking for Laparoscopic Surgery

• 🔥 Laparoscopic surgery video desmoking (LSVD) dataset is now available.
• TODO: Release pretrained model and codes.

In this work, we suggest utilizing the internal characteristics of real-world surgery videos for effective self-supervised video desmoking, and propose a SelfSVD solution.

Due to the difficulty of collecting real paired data, most existing desmoking methods train the models by synthesizing smoke, generalizing poorly to real surgical scenarios. Although a few works have explored single-image real-world desmoking in unpaired learning manners, they still encounter challenges in handling dense smoke. In this work, we address these issues together by introducing the self-supervised surgery video desmoking (SelfSVD). On the one hand, we observe that the frame captured before the activation of high-energy devices is generally clear (named pre-smoke frame, PS frame), thus it can serve as supervision for other smoky frames, making real-world self-supervised video desmoking practically feasible. On the other hand, in order to enhance the desmoking performance, we further feed the valuable information from PS frame into models, where a masking strategy and a regularization term are presented to avoid trivial solutions. In addition, we construct a real surgery video dataset for desmoking, which covers a variety of smoky scenes. Extensive experiments on the dataset show that our SelfSVD can remove smoke more effectively and efficiently while recovering more photo-realistic details than the state-of-the-art methods.

Fill Dataset Request Form via Baidu Cloud (提取码：mvam ), and contact [email protected] with this form to get LSVD dataset ( Non-institutional emails (e.g. gmail.com) are not allowed. Please provide your institutional email address.).

Input Smoky Video	Output Video

If you make use of our work, please cite our paper.

@article{SelfSVD,
  title={Self-Supervised Video Desmoking for Laparoscopic Surgery},
  author={Wu, Renlong and Zhang, Zhilu and Zhang, Shuohao and Gou, Longfei and Chen, Haobin and Zhang, Lei and Chen, Hao and Zuo, Wangmeng},
  journal={ECCV},
  year={2024}
}