Unsupervised Desmoking of Laparoscopy Images Using Multi-scale DesmokeNet

The presence of surgical smoke in laparoscopic surgery reduces the visibility of the operative field. In order to ensure better visualization, the present paper proposes an unsupervised deep learning approach for the task of desmoking of the laparoscopic images. This network builds upon generative adversarial networks (GANs) and converts laparoscopic images from smoke domain to smoke-free domain. The network comprises a new generator architecture that has an encoder-decoder structure composed of multi-scale feature extraction (MSFE) blocks at each encoder block. The MSFE blocks of the generator capture features at multiple scales to obtain a robust deep representation map and help to reduce the smoke component in the image. Further, a structure-consistency loss has been introduced to preserve the structure in the desmoked images. The proposed network is called Multi-scale DesmokeNet, which has been evaluated on the laparoscopic images obtain from Cholec80dataset. The quantitative and qualitative results shows the efficacy of the proposed Multi-scale DesmokeNet in comparison with other state-of-the-art desmoking methods.