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.

通过使用线性光照转换建模阴影效应，提出了一种深度学习方法，使用 SP-Net 和 M-Net 来预测阴影参数和阴影遮罩，并采用修补网络 I-Net 进一步优化结果，在 ISTD 数据集上测试表明，在阴影区域方面，我们的方法比现有方法提高了 20％，同时提出了一种基于补丁的弱监督阴影去除方法，并引入了一个用于评估阴影去除方法的视频数据集 SBU-Timelapse。

基于物理的阴影图像分解技术用于去除阴影

Physics-based Shadow Image Decomposition for Shadow Removal

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 on the images. We train and test
our framework on the most challenging shadow removal dataset (ISTD). Compared
to the state-of-the-art method, our model achieves a 40% error reduction in
terms of root mean square error (RMSE) for the shadow area, reducing RMSE from
13.3 to 7.9. Moreover, we create an augmented ISTD dataset based on an image
decomposition system by modifying the shadow parameters to generate new
synthetic shadow images. Training our model on this new augmented ISTD dataset
further lowers the RMSE on the shadow area to 7.4.

本研究提出了一种基于深度学习的去除影子方法，通过线性照明转换模拟影子的形成，使用 SP-Net 和 M-Net 网络分别预测影子的参数和阴影掩层，通过在 ISTD 数据集上训练，相比其他方法取得了 40％的错误率降低并使用扩充的 ISTD 数据集进一步优化了效果。