  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.
