Shadow removal is an essential task in computer vision and computer graphics. Recent
shadow removal approaches all train convolutional neural networks (CNN) on real paired …

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 …

Abstract Deep Convolutional Neural Networks (CNNs) based methods have achieved
significant breakthroughs in the task of single image shadow removal. However, the …

This paper introduces training of shadow detectors under the large-scale dataset paradigm.
This was previously impossible due to the high cost of precise shadow annotation. Instead …

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 …

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 …

The requirement for paired shadow and shadow-free images limits the size and diversity of
shadow removal datasets and hinders the possibility of training large-scale, robust shadow …

We introduce scGAN, a novel extension of conditional Generative Adversarial Networks
(GAN) tailored for the challenging problem of shadow detection in images. Previous …

Shadow removal can significantly improve the image visual quality and has many
applications in computer vision. Deep learning methods based on CNNs have become the …

Shadow removal is an essential task for scene understanding. Many studies consider only
matching the image contents, which often causes two types of ghosts: color in-consistencies …