用小波特征和自编码先验，提升阴影分割与去除效果。
WavShadow: Wavelet Based Shadow Segmentation and Removal
- 结合小波特征与自编码器先验，增强边缘与多尺度感知。
- 在DESOBA数据集上实现最快收敛与最优去阴影效果。
- 适合图像修复、自动驾驶等复杂光照场景应用。
阴影去除与分割仍是计算机视觉中的挑战性任务，尤其在复杂真实场景中。本文提出一种新方法，通过引入在Places2数据集上预训练的掩码自编码器（MAE）先验，并结合快速傅里叶卷积（FFC）模块，显著加快了模型收敛速度并提升了性能。主要创新包括：(1) 利用在Places2上训练的MAE先验以增强上下文理解；(2) 采用哈尔小波特征以提升边缘检测与多尺度分析能力；(3) 设计改进的SAM适配器以实现鲁棒的阴影分割。在具有挑战性的DESOBA数据集上的大量实验表明，该方法达到当前最佳表现，显著提升了收敛速度与去阴影质量。
原文摘要 · Abstract (English)
Shadow removal and segmentation remain challenging tasks in computer vision, particularly in complex real world scenarios. This study presents a novel approach that enhances the ShadowFormer model by incorporating Masked Autoencoder (MAE) priors and Fast Fourier Convolution (FFC) blocks, leading to significantly faster convergence and improved performance. We introduce key innovations: (1) integration of MAE priors trained on Places2 dataset for better context understanding, (2) adoption of Haar wavelet features for enhanced edge detection and multiscale analysis, and (3) implementation of a modified SAM Adapter for robust shadow segmentation. Extensive experiments on the challenging DESOBA dataset demonstrate that our approach achieves state of the art results, with notable improvements in both convergence speed and shadow removal quality.
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