Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter

TL;DR

This paper introduces ULW, a deep learning approach combining a novel loss function and a differentiable Wiener filter to effectively remove surgical smoke from laparoscopic images, enhancing clarity for surgeons and computer-assisted systems.

Contribution

The paper presents a new U-Net based method with a combined loss function and integrated differentiable Wiener filter for real-time surgical smoke removal.

Findings

ULW outperforms existing methods in visual clarity and quantitative metrics.

The combined loss function improves image quality by integrating structural, perceptual, and pixel-wise information.

The learnable Wiener filter effectively models smoke degradation, enabling better image restoration.

Abstract

Laparoscopic surgeries often suffer from reduced visual clarity due to the presence of surgical smoke originated by surgical instruments, which poses significant challenges for both surgeons and vision based computer-assisted technologies. In order to remove the surgical smoke, a novel U-Net deep learning with new loss function and integrated differentiable Wiener filter (ULW) method is presented. Specifically, the new loss function integrates the pixel, structural, and perceptual properties. Thus, the new loss function, which combines the structural similarity index measure loss, the perceptual loss, as well as the mean squared error loss, is able to enhance the quality and realism of the reconstructed images. Furthermore, the learnable Wiener filter is capable of effectively modelling the degradation process caused by the surgical smoke. The effectiveness of the proposed ULW method is evaluated using the publicly available paired laparoscopic smoke and smoke-free image dataset, which provides reliable benchmarking and quantitative comparisons. Experimental results show that the proposed ULW method excels in both visual clarity and metric-based evaluation. As a result, the proposed ULW method offers a promising solution for real-time enhancement of laparoscopic imagery. The code is available at https://github.com/chengyuyang-njit/ImageDesmoke.