Single-image shadow removal is a significant task that is still unresolved. Most existing deep learning-based approaches attempt to remove the shadow directly, which can not deal with the shadow well. To handle this issue, we consider removing the shadow in a coarse-to-fine fashion and propose a simple but effective Progressive Recurrent Network (PRNet). The network aims to remove the shadow progressively, enabing us to flexibly adjust the number of iterations to strike a balance between performance and time. Our network comprises two parts: shadow feature extraction and progressive shadow removal. Specifically, the first part is a shallow ResNet which constructs the representations of the input shadow image on its original size, preventing the loss of high-frequency details caused by the downsampling operation. The second part has two critical components: the re-integration module and the update module. The proposed re-integration module can fully use the outputs of the previous iteration, providing input for the update module for further shadow removal. In this way, the proposed PRNet makes the whole process more concise and only uses 29% network parameters than the best published method. Extensive experiments on the three benchmarks, ISTD, ISTD+, and SRD, demonstrate that our method can effectively remove shadows and achieve superior performance.

翻译：单幅图像阴影去除是一项尚未解决的重要任务。现有基于深度学习的多数方法试图直接去除阴影，但处理效果欠佳。针对这一问题，我们采用由粗到精的去除策略，提出了一种简单而有效的渐进式循环网络（PRNet）。该网络旨在逐步去除阴影，从而能够灵活调整迭代次数以平衡性能与时效。网络由两部分组成：阴影特征提取与渐进式阴影去除。具体而言，第一部分采用浅层ResNet，在原始尺寸上构建输入阴影图像的表示，从而避免下采样操作导致的高频细节损失。第二部分包含两个关键组件：重组模块与更新模块。所提出的重组模块能充分利用前一次迭代的输出，为更新模块提供进一步去除阴影的输入。通过这种设计，PRNet使整个过程更为简洁，且参数量仅为此前最优方法的29%。在ISTD、ISTD+和SRD三个基准数据集上的广泛实验表明，本方法能有效去除阴影并实现优越性能。