43-Issue 7

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https://diglib.eg.org/handle/10.2312/3607047

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Browsing 43-Issue 7 by Subject "aware"

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    Density-Aware Diffusion Model for Efficient Image Dehazing
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhang, Ling; Bai, Wenxu; Xiao, Chunxia; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    Existing image dehazing methods have made remarkable progress. However, they generally perform poorly on images with dense haze, and often suffer from unsatisfactory results with detail degradation or color distortion. In this paper, we propose a density-aware diffusion model (DADM) for image dehazing. Guided by the haze density, our DADM can handle images with dense haze and complex environments. Specifically, we introduce a density-aware dehazing network (DADNet) in the reverse diffusion process, which can help DADM gradually recover a clear haze-free image from a haze image. To improve the performance of the network, we design a cross-feature density extraction module (CDEModule) to extract the haze density for the image and a density-guided feature fusion block (DFFBlock) to learn the effective contextual features. Furthermore, we introduce an indirect sampling strategy in the test sampling process, which not only suppresses the accumulation of errors but also ensures the stability of the results. Extensive experiments on popular benchmarks validate the superior performance of the proposed method. The code is released in https://github.com/benchacha/DADM.
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    Frequency-Aware Facial Image Shadow Removal through Skin Color and Texture Learning
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhang, Ling; Xie, Wenyang; Xiao, Chunxia; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    Existing facial image shadow removal methods predominantly rely on pre-extracted facial features. However, these methods often fail to capitalize on the full potential of these features, resorting to simplified utilization. Furthermore, they tend to overlook the importance of low-frequency information during the extraction of prior features, which can be easily compromised by noises. In our work, we propose a frequency-aware shadow removal network (FSRNet) for facial image shadow removal, which utilizes the skin color and texture information in the face to help recover illumination in shadow regions. Our FSRNet uses a frequencydomain image decomposition network to extract the low-frequency skin color map and high-frequency texture map from the face images, and applies a color-texture guided shadow removal network to produce final shadow removal result. Concretely, the designed fourier sparse attention block (FSABlock) can transform images from the spatial domain to the frequency domain and help the network focus on the key information. We also introduce a skin color fusion module (CFModule) and a texture fusion module (TFModule) to enhance the understanding and utilization of color and texture features, promoting high-quality result without color distortion and detail blurring. Extensive experiments demonstrate the superiority of the proposed method. The code is available at https://github.com/laoxie521/FSRNet.