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

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The
cup to disc ratio (CDR) plays an important role in the screening and diagnosis
of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD)
and optic cup (OC) from fundus images is a fundamental task. Most existing
methods segment them separately, and rely on hand-crafted visual feature from
fundus images. In this paper, we propose a deep learning architecture, named
M-Net, which solves the OD and OC segmentation jointly in a one-stage
multi-label system. The proposed M-Net mainly consists of multi-scale input
layer, U-shape convolutional network, side-output layer, and multi-label loss
function. The multi-scale input layer constructs an image pyramid to achieve
multiple level receptive field sizes. The U-shape convolutional network is
employed as the main body network structure to learn the rich hierarchical
representation, while the side-output layer acts as an early classifier that
produces a companion local prediction map for different scale layers. Finally,
a multi-label loss function is proposed to generate the final segmentation map.
For improving the segmentation performance further, we also introduce the polar
transformation, which provides the representation of the original image in the
polar coordinate system. The experiments show that our M-Net system achieves
state-of-the-art OD and OC segmentation result on ORIGA dataset.
Simultaneously, the proposed method also obtains the satisfactory glaucoma
screening performances with calculated CDR value on both ORIGA and SCES
datasets.

本文提出了 M-Net，这是一种深度学习架构，可以在一个多标签系统中联合解决视盘和视杯分割的问题，并使用极坐标变换进一步提高了分割性能。实验结果表明，所提出的方法在 ORIGA 数据集上取得了最先进的视盘和视杯分割效果，同时还在 ORIGA 数据集和 SCES 数据集上获得了令人满意的青光眼筛查表现。