Liu Jiawei, Wang Qiang, Fan Huijie, Tian Jiandong, Tang Yandong
IEEE Trans Neural Netw Learn Syst. 2024 Nov;35(11):15857-15871. doi: 10.1109/TNNLS.2023.3290078. Epub 2024 Oct 29.
The current shadow removal pipeline relies on the detected shadow masks, which have limitations for penumbras and tiny shadows, and results in an excessively long pipeline. To address these issues, we propose a shadow imaging bilinear model and design a novel three-branch residual (TBR) network for shadow removal. Our bilinear model reveals the single-image shadow removal process and can explain why simply increasing the brightness of shadow areas cannot remove shadows without artifacts. We considerably shorten the shadow removal pipeline by modeling illumination compensation and developing a single-stage shadow removal network without additional detection and refinement networks. Specifically, our network consists of three task branches, i.e., shadow image reconstruction, shadow matte estimation, and shadow removal. To merge these three branches and enhance the shadow removal branch, we design a model-based TBR module. Multiple TBR modules are cascaded to generate an intensive information flow and facilitate feature integration among the three branches. Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. The model and code are available at https://github.com/nachifur/TBRNet.
当前的阴影去除流程依赖于检测到的阴影掩码，这些掩码在处理半影和微小阴影时存在局限性，并且会导致流程过长。为了解决这些问题，我们提出了一种阴影成像双线性模型，并设计了一种新颖的三分支残差（TBR）网络用于阴影去除。我们的双线性模型揭示了单图像阴影去除过程，并能解释为什么简单地增加阴影区域的亮度无法在不产生伪影的情况下去除阴影。通过对光照补偿进行建模并开发一个无需额外检测和细化网络的单阶段阴影去除网络，我们大大缩短了阴影去除流程。具体来说，我们的网络由三个任务分支组成，即阴影图像重建、阴影蒙版估计和阴影去除。为了融合这三个分支并增强阴影去除分支，我们设计了一个基于模型的TBR模块。多个TBR模块级联以生成密集的信息流，并促进三个分支之间的特征整合。因此，我们的网络通过三分支协作确保了非阴影区域的保真度，并恢复了阴影区域的光强度。大量实验表明，我们的方法优于现有方法。模型和代码可在https://github.com/nachifur/TBRNet获取。
