Attention-Aware Laparoscopic Image Desmoking Network with Lightness
  Embedding and Hybrid Guided Embedding
Ziteng Liu Jiahua Zhu Bainan Liu Hao Liu Wenpeng Gao Yili Fu
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