Generative Smoke Removal
This addresses visibility issues for surgeons and errors in surgical navigation systems, but it is incremental as it builds on existing generative methods.
The paper tackled the problem of smoke obscuring surgical images by proposing a modified GAN architecture with a perceptual loss function, resulting in efficient smoke removal while preserving image quality.
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.