Dsmk-DcSeg-Lap, a Generative Adversarial Network Guided by Dark-Chanel and Segmentation to Smoke Removal in Laparoscopic Images

Author:

Moreno Hugo,Salazar-Colores Sebastián,Valentín Luis M.,Flores Gerardo

Publisher

Springer International Publishing

Reference33 articles.

1. Rizzo, G., Cuccurullo, D., Iacobelli, G., & et al. (2019). Smoke evacuation systems in laparoscopic surgery: A systematic review and meta-analysis. Surgical Endoscopy, 33(11), 4507–4516.

2. Bhatia, S., Singh, N., Kaur, S., & et al. (2020). Smoke management in laparoscopy: a comprehensive review. Journal of Minimal Access Surgery, 16(1), 18.

3. Fernandez-Ruiz, J., Perez-de Luque, A., Perez-Castellanos, J. L., & et al. (2021). Novel smoke evacuation systems for laparoscopy: A systematic review. Journal of Laparoendoscopic and Advanced Surgical Techniques, 31(1), 1–10.

4. Smith, J., & Johnson, A. (2010). Smoke removal from images using adaptive filtering. IEEE Transactions on Image Processing, 19(5), 1125–1133.

5. Johnson, R., & Lee, H. (2012). Image dehazing using non-linear techniques. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1674–1681). IEEE.

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