Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Visibility,Visual inspection,Laparoscopic surgery,Pattern recognition,Computer science,Smoke,Image quality,Image processing,Image-guided surgery,Augmented Lagrangian method,Artificial intelligence |
DocType | Volume | Citations |
Journal | abs/1803.08410 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congcong Wang | 1 | 6 | 3.82 |
| Faouzi Alaya Cheikh | 2 | 168 | 38.47 |
| Mounir Kaaniche | 3 | 74 | 13.41 |
| Ole Jakob Elle | 4 | 68 | 15.39 |