Progressive Frequency-Aware Network for Laparoscopic Image Desmoking
Jiale Zhang Wenfeng Huang Xiangyun Liao Qiong Wang
Laparoscopic surgery offers minimally invasive procedures with better patient
outcomes, but smoke presence challenges visibility and safety. Existing
learning-based methods demand large datasets and high computational resources.
We propose the Progressive Frequency-Aware Network (PFAN), a lightweight GAN
framework for laparoscopic image desmoking, combining the strengths of CNN and
Transformer for progressive information extraction in the frequency domain.
PFAN features CNN-based Multi-scale Bottleneck-Inverting (MBI) Blocks for
capturing local high-frequency information and Locally-Enhanced Axial Attention
Transformers (LAT) for efficiently handling global low-frequency information.
PFAN efficiently desmokes laparoscopic images even with limited training data.
Our method outperforms state-of-the-art approaches in PSNR, SSIM, CIEDE2000,
and visual quality on the Cholec80 dataset and retains only 629K parameters.
Our code and models are made publicly available at:
https://github.com/jlzcode/PFAN.