Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding
Abstract: This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in providing vital information pertaining to smoke density. The reconstruction of smoke-free image is guided by a hybrid embedding, which combines the estimated smoke mask with the initial image. Experimental results demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that is $2.79\%$ higher than the state-of-the-art methods, while also exhibits a remarkable $38.2\%$ reduction in run-time. Overall, the proposed method offers comparable or even superior performance in terms of both smoke removal quality and computational efficiency when compared to existing state-of-the-art methods. This work will be publicly available on http://homepage.hit.edu.cn/wpgao
- “Joint desmoking and denoising of laparoscopy images,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016, pp. 1050–1054.
- “A lumen-adapted navigation scheme with spatial awareness from monocular vision for autonomous robotic endoscopy,” Robotics and Autonomous Systems, vol. 165, pp. 104444, 2023.
- “A geometry-aware deep network for depth estimation in monocular endoscopy,” Engineering Applications of Artificial Intelligence, vol. 122, pp. 105989, 2023.
- “Optics of the Atmosphere: Scattering by Molecules and Particles,” Physics Today, vol. 30, no. 5, pp. 76–77, 05 1977.
- “Single image haze removal using dark channel prior,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 12, pp. 2341–2353, 2010.
- “De-smokegcn: generative cooperative networks for joint surgical smoke detection and removal,” IEEE transactions on medical imaging, vol. 39, no. 5, pp. 1615–1625, 2019.
- “Aod-net: All-in-one dehazing network,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 4770–4778.
- “Griddehazenet: Attention-based multi-scale network for image dehazing,” in Proceedings of the IEEE ICCV, 2019, pp. 7314–7323.
- “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
- “Image dehazing transformer with transmission-aware 3d position embedding,” in Proceedings of the IEEE CVPR, 2022, pp. 5812–5820.
- “Vision transformers for single image dehazing,” IEEE Transactions on Image Processing, vol. 32, pp. 1927–1941, 2023.
- “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE ICCV, 2021, pp. 10012–10022.
- “Swin-unet: Unet-like pure transformer for medical image segmentation,” in ECCV. Springer, 2022, pp. 205–218.
- “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging, vol. 36, no. 1, pp. 86–97, 2017.
- “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595.
- “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695–4708, 2012.
- “Making a “completely blind” image quality analyzer,” IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209–212, 2013.
- “Blind image quality evaluation using perception based features,” in 2015 Twenty First National Conference on Communications (NCC), 2015, pp. 1–6.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.