Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding (2404.07556v1)
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.