SHARDS: Efficient SHAdow Removal using Dual Stage Network for High-Resolution Images

Shadow Removal is an important and widely researched topic in computer vision. Recent advances in deep learning have resulted in addressing this problem by using convolutional neural networks (CNNs) similar to other vision tasks. But these existing works are limited to low-resolution images. Furthermore, the existing methods rely on heavy network architectures which cannot be deployed on resource-constrained platforms like smartphones. In this paper, we propose SHARDS, a shadow removal method for high-resolution images. The proposed method solves shadow removal for high-resolution images in two stages using two lightweight networks: a Low-resolution Shadow Removal Network (LSRNet) followed by a Detail Refinement Network (DRNet). LSRNet operates at low-resolution and computes a low-resolution, shadow-free output. It achieves state-of-the-art results on standard datasets with 65x lesser network parameters than existing methods. This is followed by DRNet, which is tasked to refine the low-resolution output to a high-resolution output using the high-resolution input shadow image as guidance. We construct high-resolution shadow removal datasets and through our experiments, prove the effectiveness of our proposed method on them. It is then demonstrated that this method can be deployed on modern day smartphones and is the first of its kind solution that can efficiently (2.4secs) perform shadow removal for high-resolution images (12MP) in these devices. Like many existing approaches, our shadow removal network relies on a shadow region mask as input to the network. To complement the lightweight shadow removal network, we also propose a lightweight shadow detector in this paper.