Browsing by Author "Zhang, Ling"

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    CLA-GAN: A Context and Lightness Aware Generative Adversarial Network for Shadow Removal
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Ling; Long, Chengjiang; Yan, Qingan; Zhang, Xiaolong; Xiao, Chunxia; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    In 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.
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    Facial Image Shadow Removal via Graph-based Feature Fusion
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Zhang, Ling; Chen, Ben; Liu, Zheng; Xiao, Chunxia; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Despite natural image shadow removal methods have made significant progress, they often perform poorly for facial image due to the unique features of the face. Moreover, most learning-based methods are designed based on pixel-level strategies, ignoring the global contextual relationship in the image. In this paper, we propose a graph-based feature fusion network (GraphFFNet) for facial image shadow removal. We apply a graph-based convolution encoder (GCEncoder) to extract global contextual relationships between regions in the coarse shadow-less image produced by an image flipper. Then, we introduce a feature modulation module to fuse the global topological relation onto the image features, enhancing the feature representation of the network. Finally, the fusion decoder integrates all the effective features to reconstruct the image features, producing a satisfactory shadow-removal result. Experimental results demonstrate the superiority of the proposed GraphFFNet over the state-of-the-art and validate the effectiveness of facial image shadow removal.