对比不同损失函数与可学习维纳滤波对腹腔镜去烟效果的影响
Investigating the Impact of Various Loss Functions and Learnable Wiener Filter for Laparoscopic Image Desmoking
- 拆解复合损失与可学习维纳滤波模块，逐项测试其作用
- 移除维纳滤波后图像保真度下降，各指标均显著降低
- 适合关注医学图像增强中组件贡献的视觉算法研究者
为严格评估近期提出的ULW框架中各组件的有效性与必要性，本文开展全面消融实验。该框架结合基于U-Net的主干网络、包含均方误差（MSE）、结构相似性指数（SSIM）和感知损失的复合损失函数，以及可微分的可学习维纳滤波模块。本研究系统性地移除可学习维纳滤波模块，并分别选用复合损失中的单一损失项进行测试。所有变体在公开的成对腹腔镜图像数据集上通过定量指标（SSIM、PSNR、MSE和CIEDE-2000）及定性视觉对比进行评估。
原文摘要 · Abstract (English)
To rigorously assess the effectiveness and necessity of individual components within the recently proposed ULW framework for laparoscopic image desmoking, this paper presents a comprehensive ablation study. The ULW approach combines a U-Net based backbone with a compound loss function that comprises mean squared error (MSE), structural similarity index (SSIM) loss, and perceptual loss. The framework also incorporates a differentiable, learnable Wiener filter module. In this study, each component is systematically ablated to evaluate its specific contribution to the overall performance of the whole framework. The analysis includes: (1) removal of the learnable Wiener filter, (2) selective use of individual loss terms from the composite loss function. All variants are benchmarked on a publicly available paired laparoscopic images dataset using quantitative metrics (SSIM, PSNR, MSE and CIEDE-2000) alongside qualitative visual comparisons.
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