利用阴影固有频域特性，无监督实现更精准的阴影去除。
FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors
- 通过小波注意力下采样模块分解图像频域特征，增强特定频段阴影细节。
- 在AISTD和SRD数据集上达到当前最优性能，阴影区域恢复更完整。
- 适合需要无标注训练的阴影去除场景，如真实图像修复与增强。
阴影去除因几何、光照与环境因素的复杂交互而具有挑战性。现有无监督方法常忽略阴影特有先验，导致阴影恢复不完全。为此，本文提出一种新型无监督频域感知阴影去除网络（FASR-Net），利用阴影区域的固有频率特性。具体地，提出的波形注意力下采样模块（WADM）结合小波图像分解与可变形注意力机制，有效将图像分解为频域成分，增强特定频带内的阴影细节。同时引入多个新损失函数以实现精确的无阴影图像重建：频率损失用于捕捉图像分量细节，亮度-色度损失参考无阴影区域的色度信息，对齐损失则确保阴影区与无阴影区之间过渡平滑。在AISTD和SRD数据集上的实验结果表明，该方法在阴影去除性能上优于现有方法。
原文摘要 · Abstract (English)
Shadow removal is challenging due to the complex interaction of geometry, lighting, and environmental factors. Existing unsupervised methods often overlook shadow-specific priors, leading to incomplete shadow recovery. To address this issue, we propose a novel unsupervised Frequency Aware Shadow Removal Network (FASR-Net), which leverages the inherent frequency characteristics of shadow regions. Specifically, the proposed Wavelet Attention Downsampling Module (WADM) integrates wavelet-based image decomposition and deformable attention, effectively breaking down the image into frequency components to enhance shadow details within specific frequency bands. We also introduce several new loss functions for precise shadow-free image reproduction: a frequency loss to capture image component details, a brightness-chromaticity loss that references the chromaticity of shadow-free regions, and an alignment loss to ensure smooth transitions between shadowed and shadow-free regions. Experimental results on the AISTD and SRD datasets demonstrate that our method achieves superior shadow removal performance.
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