  In minimally invasive surgery, the use of tissue dissection tools causes
smoke, which inevitably degrades the image quality. This could reduce the
visibility of the operation field for surgeons and introduces errors for the
computer vision algorithms used in surgical navigation systems. In this paper,
we propose a novel approach for computational smoke removal using supervised
image-to-image translation. We demonstrate that straightforward application of
existing generative algorithms allows removing smoke but decreases image
quality and introduces synthetic noise (grid-structure). Thus, we propose to
solve this issue by modification of GAN's architecture and adding perceptual
image quality metric to the loss function. Obtained results demonstrate that
proposed method efficiently removes smoke as well as preserves perceptually
sufficient image quality.
