Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding

• URL: http://arxiv.org/abs/2404.07556v1
• Date: Thu, 11 Apr 2024 08:36:36 GMT
• Title: Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding
• Authors: Ziteng Liu, Jiahua Zhu, Bainan Liu, Hao Liu, Wenpeng Gao, Yili Fu,
• Abstract summary: A two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The proposed method boasts a Peak Signal to Noise Ratio that is $2.79%$ higher than the state-of-the-art methods.
• Score: 9.909043664967063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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