  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.
