Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data

TL;DR

This paper introduces LG-ShadowNet, a lightness-guided deep learning model trained on unpaired data, which effectively removes shadows from images and outperforms existing methods.

Contribution

The paper proposes a novel lightness-guided network architecture trained on unpaired data for shadow removal, improving performance over prior methods.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effective training on unpaired data

Utilizes a new loss function based on color prior

Abstract

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 most effective approach for shadow removal by training on either paired data, where both the shadow and underlying shadow-free versions of an image are known, or unpaired data, where shadow and shadow-free training images are totally different with no correspondence. In practice, CNN training on unpaired data is more preferred given the easiness of training data collection. In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data. In this method, we first train a CNN module to compensate for the lightness and then train a second CNN module with the guidance of lightness information from the first CNN module for final shadow removal. We also introduce a loss function to further utilise the colour prior of existing data. Extensive experiments on widely used ISTD, adjusted ISTD and USR datasets demonstrate that the proposed method outperforms the state-of-the-art methods with training on unpaired data.