Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel
This addresses a critical issue for surgeons and patients by enhancing real-time visibility in medical procedures, though it appears incremental as it builds on existing desmoking and dehazing methods.
The paper tackles the problem of smoke obscuring visibility in laparoscopic surgery images by introducing a novel computational approach using an image-to-image conditional generative adversarial network guided by an embedded dark channel, achieving improved performance in desmoking with a processing speed of 92 frames per second.
In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.