Shadow Removal by High-Quality Shadow Synthesis
Yunshan Zhong, Lizhou You, Yuxin Zhang, Fei Chao, Yonghong Tian,, Rongrong Ji

TL;DR
This paper introduces HQSS, a novel framework for high-quality shadow image synthesis that improves shadow removal by generating more authentic and detailed pseudo shadow images, outperforming existing methods.
Contribution
The paper proposes a new shadow synthesis framework, HQSS, which decouples images into shadow and non-shadow regions and employs specialized losses to produce more realistic shadow images.
Findings
HQSS outperforms state-of-the-art methods on multiple datasets.
The framework effectively retains shadow characteristics and image details.
HQSS improves the quality of pseudo shadow images for shadow removal tasks.
Abstract
Most shadow removal methods rely on the invasion of training images associated with laborious and lavish shadow region annotations, leading to the increasing popularity of shadow image synthesis. However, the poor performance also stems from these synthesized images since they are often shadow-inauthentic and details-impaired. In this paper, we present a novel generation framework, referred to as HQSS, for high-quality pseudo shadow image synthesis. The given image is first decoupled into a shadow region identity and a non-shadow region identity. HQSS employs a shadow feature encoder and a generator to synthesize pseudo images. Specifically, the encoder extracts the shadow feature of a region identity which is then paired with another region identity to serve as the generator input to synthesize a pseudo image. The pseudo image is expected to have the shadow feature as its input shadow…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Neural Network Applications · Image Enhancement Techniques
