Progressive Frequency-Aware Network for Laparoscopic Image Desmoking

• URL: http://arxiv.org/abs/2312.12023v1
• Date: Tue, 19 Dec 2023 10:19:44 GMT
• Title: Progressive Frequency-Aware Network for Laparoscopic Image Desmoking
• Authors: Jiale Zhang and Wenfeng Huang, Xiangyun Liao, and Qiong Wang
• Abstract summary: We propose a lightweight GAN framework for laparoscopic image desmoking, combining the strengths of CNN and Transformer. 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.
• Score: 8.988060012957497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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