区分硬阴影与软阴影，用双路网络分别处理并提升细节还原
Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network and Vision Transformer
- 设计双分支结构，分别处理硬阴影和软阴影
- 在ISTD数据集上达到2.905的RMSE，优于现有方法
- 结合Vision Transformer与UNet++，增强边缘与特征融合
图像阴影去除是计算机视觉中的关键任务。真实场景中，阴影会改变图像的颜色和亮度，影响感知与纹理识别。传统及深度学习方法常忽视硬阴影与软阴影的不同处理需求，缺乏对两类阴影的精细处理。本文提出一种双路径模型，通过专门设计的损失函数分别处理硬阴影与软阴影，根据阴影类型选择对应路径生成无阴影输出，并融合Vision Transformer与UNet++以提升边缘细节与特征融合能力。实验表明，该模型在ISTD数据集上取得2.905的RMSE值，显著优于典型单路径方法。
原文摘要 · Abstract (English)
Image shadow removal is a crucial task in computer vision. In real-world scenes, shadows alter image color and brightness, posing challenges for perception and texture recognition. Traditional and deep learning methods often overlook the distinct needs for handling hard and soft shadows, thereby lacking detailed processing to specifically address each type of shadow in images.We propose a dual-path model that processes these shadows separately using specially designed loss functions to accomplish the hard and soft shadow removal. The model classifies shadow types and processes them through appropriate paths to produce shadow-free outputs, integrating a Vision Transformer with UNet++ for enhanced edge detail and feature fusion. Our model outperforms state-of-the-art methods and achieves 2.905 RMSE value on the ISTD dataset, which demonstrates greater effectiveness than typical single-path approaches.
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