# Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel

arXiv: https://arxiv.org/abs/2004.08947 · HTML: https://mlsanity.com/paper/2004.08947/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Self-Supervised Video Desmoking for Laparoscopic Surgery](https://mlsanity.com/paper/2403.11192/) (2024)
- [SmokeSeer: 3D Gaussian Splatting for Smoke Removal and Scene Reconstruction](https://mlsanity.com/paper/2509.17329/) (2025)
- [LSD3K: A Benchmark for Smoke Removal from Laparoscopic Surgery Images](https://mlsanity.com/paper/2407.13132/) (2024)
- [Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter](https://mlsanity.com/paper/2505.21634/) (2025)
- [Benchmarking Endoscopic Surgical Image Restoration and Beyond](https://mlsanity.com/paper/2505.19161/) (2025)
- [SurgiATM: A Physics-Guided Plug-and-Play Model for Deep Learning-Based Smoke Removal in Laparoscopic Surgery](https://mlsanity.com/paper/2511.05059/) (2025)
- [RGA-Net: A Vision Enhancement Framework for Robotic Surgical Systems Using Reciprocal Attention Mechanisms](https://mlsanity.com/paper/2602.13726/) (2026)
- [PhySe-RPO: Physics and Semantics Guided Relative Policy Optimization for Diffusion-Based Surgical Smoke Removal](https://mlsanity.com/paper/2603.22844/) (2026)
- [Seeing Through Smoke: Surgical Desmoking for Improved Visual Perception](https://mlsanity.com/paper/2603.25867/) (2026)

## Abstract (arXiv)

In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the CO2 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.