  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}.
