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 approach for instance segmen- tation given an image of
homogeneous object clus- ter (HOC). Our learning approach is one-shot be- cause
a single video of an object instance is cap- tured and it requires no human
annotation. Our in- tuition is that images of homogeneous objects can be
effectively synthesized based on structure and illumination priors derived from
real images. A novel solver is proposed that iteratively maximizes our
structured likelihood to generate realistic im- ages of HOC. Illumination
transformation scheme is applied to make the real and synthetic images share
the same illumination condition. Extensive experiments and comparisons are
performed to ver- ify our method. We build a dataset consisting of pixel-level
annotated images of HOC. The dataset and code will be published with the paper.

本文提出了一种单镜头拍摄无需人工标注的全新方法，包括学习方法、新型的求解器、光照变换方案，并建立了一个由像素级注释的同质对象簇数据集，对这一方法进行了广泛的实验和比较。