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dc.contributor.authorZhang, Lingen_US
dc.contributor.authorLong, Chengjiangen_US
dc.contributor.authorYan, Qinganen_US
dc.contributor.authorZhang, Xiaolongen_US
dc.contributor.authorXiao, Chunxiaen_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.description.abstractIn this paper, we propose a novel context and lightness aware Generative Adversarial Network (CLA-GAN) framework for shadow removal, which refines a coarse result to a final shadow removal result in a coarse-to-fine fashion. At the refinement stage, we first obtain a lightness map using an encoder-decoder structure. With the lightness map and the coarse result as the inputs, the following encoder-decoder tries to refine the final result. Specifically, different from current methods restricted pixel-based features from shadow images, we embed a context-aware module into the refinement stage, which exploits patch-based features. The embedded module transfers features from non-shadow regions to shadow regions to ensure the consistency in appearance in the recovered shadow-free images. Since we consider pathces, the module can additionally enhance the spatial association and continuity around neighboring pixels. To make the model pay more attention to shadow regions during training, we use dynamic weights in the loss function. Moreover, we augment the inputs of the discriminator by rotating images in different degrees and use rotation adversarial loss during training, which can make the discriminator more stable and robust. Extensive experiments demonstrate the validity of the components in our CLA-GAN framework. Quantitative evaluation on different shadow datasets clearly shows the advantages of our CLA-GAN over the state-of-the-art methods.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectShadow Removal
dc.titleCLA-GAN: A Context and Lightness Aware Generative Adversarial Network for Shadow Removalen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersImage Restoration

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  • 39-Issue 7
    Pacific Graphics 2020 - Symposium Proceedings

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