Self-Supervised Video Desmoking for Laparoscopic Surgery
<|reference_start|>Self-Supervised Video Desmoking for Laparoscopic Surgery: 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. The dataset, codes, and pre-trained models are available at \url{https://github.com/ZcsrenlongZ/SelfSVD}.<|reference_end|>
@article{wu2024self-supervised,
    title={Self-Supervised Video Desmoking for Laparoscopic Surgery},
    author={Renlong Wu, Zhilu Zhang, Shuohao Zhang, Longfei Gou, Haobin Chen, Lei
  Zhang, Hao Chen, Wangmeng Zuo},
    journal={arXiv preprint arXiv:2403.11192},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11192},
    primaryClass={cs.CV}
}