A Smoke Removal Method for Laparoscopic Images
This addresses visibility and accuracy issues for surgeons and image-guided surgery systems, but it is incremental as it builds on existing variational approaches with specific priors.
The paper tackles the problem of surgical smoke degrading laparoscopic image quality by proposing a variational method to decompose images into smoke and enhanced components, resulting in effective smoke removal as shown by quantitative metrics like FADE, JNBM, and RE and qualitative visual inspection.
In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces error for the image processing (used in image guided surgery), but also reduces the visibility of the surgeons. In this paper, we propose to enhance the laparoscopic images by decomposing them into unwanted smoke part and enhanced part using a variational approach. The proposed method relies on the observation that smoke has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained unwanted smoke component is then subtracted from the original degraded image, resulting in the enhanced image. The obtained quantitative scores in terms of FADE, JNBM and RE metrics show that our proposed method performs rather well. Furthermore, the qualitative visual inspection of the results show that it removes smoke effectively from the laparoscopic images.