A Smoke Removal Method Based on Combined Data and Modified U-Net for Endoscopic Images

In minimally invasive surgery, the ablation of human tissue will produce a lot of smoke, which will interfere with the surgeon’s operation. We propose a smoke removal method based on combined data and modified U-net for endoscopic images. The real dataset and the synthetic dataset are built using a small amount of images with smoke. The real dataset is combined with the synthetic dataset successively. Qualitative evaluation shows that the quality of the output smoke-free image is the best when training using the combined data, compared to using only either the real dataset or the synthetic dataset above. Quantitative evaluation shows that the effect of smoke removal is still the best when training using the combined data in our method.Clinical Relevance—A real-time smoke removal method suitable for endoscopic surgery is proposed to help surgeons get clear images in real time and make the operation go smoothly.