Unsupervised Desmoking of Laparoscopy Images Using Multi-scale DesmokeNet

Vishal, V.; Venkatesh, Varun; Lochan, Kshetrimayum; Sharma, Neeraj; Singh, Munendra

doi:10.1007/978-3-030-40605-9_36

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…(a) Traditional image processing techniques [7]- [11], (b) Physics-model-based methods, especially the atmospheric scattering model and the dark channel prior (DCP) [2], [12], [13], (c) Artificial intelligence (AI) methods based on convolu- tional neural networks and generative networks [15]- [19], [22].…”

Section: Introductionmentioning

confidence: 99%

“…In [19], the authors implement an unsupervised framework for learning smoke removal that uses a fully convolutional encoderdecoder network to generate the same size desmoked image. In [22], an unsupervised deep learning technique based on a GAN converts laparoscopic images from the smoke domain to the smoke-free domain. The network comprises a generator architecture endowed with an encoder-decoder structure composed of multi-scale feature extraction at each encoder block.…”

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Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel

et al. 2020

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel

et al. 2020

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A desmoking algorithm for endoscopic images based on improved U‐Net model

Lin

Jiang

Pang

et al. 2021

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In laparoscopic surgery, the smoke generated by operations including electrocautery and laser ablation seriously degrades the quality of endoscopic images. It not only reduces the visibility of the surgery, leading to increased risk of surgery, but also affects the performance of image processing in computer‐assisted surgery such as segmentation, 3D reconstruction and tracking. Therefore, a desmoking algorithm is required to eliminate smoke in endoscopic images. In this article, we study a U‐Net model that can eliminate smoke of the laparoscopic image in real‐time and preserve the natural appearance of the organ surface. Our method is based on the improved U‐Net in which convolutional block attention module is used as an embedded guide mask of the decoder part. The laparoscopic image dataset is provided by the Hamlyn Center, and the Blender software is used to simulate various situations of smoke added to the laparoscopic image for training and testing. For the proposed method, the peak signal‐to‐noise ratio value is up to 29.27 and the structural similarity index is up to 0.945 over the test images. The experimental results demonstrate that the proposed method achieves a better performance than other six existing methods, which is applicable for real‐time endoscopic image processing.

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