用于腹腔镜图像去烟的渐进式频率感知网络
Progressive Frequency-Aware Network for Laparoscopic Image Desmoking
AI总结：
针对腹腔镜手术中烟雾影响视野、现有去烟方法数据与算力需求高的问题，提出结合CNN与Transformer的轻量级频域渐进去烟网络PFAN，在有限数据下实现更优去烟效果且参数量极低。
摘要：
腹腔镜手术可实现微创操作，改善患者预后，但烟雾的存在会影响视野清晰度与手术安全性。现有基于学习的方法需要大量数据集与高计算资源。我们提出渐进式频率感知网络（Progressive Frequency-Aware Network，PFAN）——一种用于腹腔镜图像去烟的轻量级GAN框架，结合CNN与Transformer的优势，在频域中进行渐进式信息提取。PFAN包含基于CNN的多尺度瓶颈反转（Multi-scale Bottleneck-Inverting，MBI）模块，用于捕获局部高频信息；以及局部增强轴向注意力Transformer（Locally-Enhanced Axial Attention Transformers，LAT），用于高效处理全局低频信息。即使训练数据有限，PFAN也能高效完成腹腔镜图像去烟。在Cholec80数据集上，我们的方法在PSNR、SSIM、CIEDE2000指标及视觉质量上均优于当前最优方法，且参数量仅为629K。我们的代码与模型已公开发布于：https://github.com/jlzcode/PFAN。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
