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 multi-scale 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.

翻译：阴影去除与分割在计算机视觉中仍是具有挑战性的任务，尤其在复杂的真实场景下。本研究提出了一种新颖方法，通过引入掩码自编码器先验知识与快速傅里叶卷积模块来增强ShadowFormer模型，从而显著加快收敛速度并提升性能。我们引入了三项关键创新：（1）集成在Places2数据集上训练的MAE先验知识以提升上下文理解能力；（2）采用Haar小波特征以增强边缘检测与多尺度分析；（3）实现改进的SAM适配器以实现鲁棒的阴影分割。在具有挑战性的DESOBA数据集上进行的大量实验表明，我们的方法取得了最先进的结果，在收敛速度与阴影去除质量方面均获得显著提升。