论文标题
基于物理的阴影图像去除阴影的分解
Physics-based Shadow Image Decomposition for Shadow Removal
论文作者
论文摘要
我们提出了一种新颖的深度学习方法，用于去除阴影。受阴影形成的物理模型的启发，我们使用线性照明转换来对图像中的阴影效应进行建模，从而允许阴影图像作为无阴影图像，阴影参数和哑光层的组合表示。我们使用两个深网，即SP-NET和M-NET，分别预测阴影参数和阴影哑光。该系统使我们可以从图像中删除阴影效果。然后，我们采用一个介绍网络I-NET来进一步完善结果。我们在最具挑战性的影子删除数据集（ISTD）上训练和测试框架。我们的方法将阴影区域的根平方误差（RMSE）提高了20 \％。此外，这种分解使我们能够制定基于贴片的弱监督阴影去除方法。与最先进的方法相比，该模型无需任何无阴影图像（很麻烦），并实现竞争性阴影删除结果，与经过完全配对的影像和无阴影图像相比，可以实现竞争性的阴影删除结果。最后，我们介绍了SBU-Timelapse，这是一个视频阴影删除数据集，用于评估阴影去除方法。
We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects from images. We then employ an inpainting network, I-Net, to further refine the results. We train and test our framework on the most challenging shadow removal dataset (ISTD). Our method improves the state-of-the-art in terms of root mean square error (RMSE) for the shadow area by 20\%. Furthermore, this decomposition allows us to formulate a patch-based weakly-supervised shadow removal method. This model can be trained without any shadow-free images (that are cumbersome to acquire) and achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. Last, we introduce SBU-Timelapse, a video shadow removal dataset for evaluating shadow removal methods.
