Browsing by Author "Zhang, Qing"

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    Learning Multi-Scale Deep Image Prior for High-Quality Unsupervised Image Denoising
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Jiang, Hao; Zhang, Qing; Nie, Yongwei; Zhu, Lei; Zheng, Wei-Shi; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Recent methods on image denoising have achieved remarkable progress, benefiting mostly from supervised learning on massive noisy/clean image pairs and unsupervised learning on external noisy images. However, due to the domain gap between the training and testing images, these methods typically have limited applicability on unseen images. Although several attempts have been made to avoid the domain gap issue by learning denoising from singe noisy image itself, they are less effective in handling real-world noise because of assuming the noise corruptions are independent and zero mean. In this paper, we go step further beyond prior work by presenting a novel unsupervised image denoising framework trained from single noisy image without making any explicit assumptions on the noise statistics. Our approach is built upon the deep image prior (DIP), which enables diverse image restoration tasks. However, as is, the denoising performance of DIP will significantly deteriorate on nonzero- mean noise and is sensitive to the number of iterations. To overcome this problem, we propose to utilize multi-scale deep image prior by imposing DIP across different image scales under the constraint of a scale consistency. Experiments on synthetic and real datasets demonstrate that our method performs favorably against the state-of-the-art methods for image denoising.
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    Specular Highlight Removal for Real-world Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Fu, Gang; Zhang, Qing; Song, Chengfang; Lin, Qifeng; Xiao, Chunxia; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Removing specular highlight in an image is a fundamental research problem in computer vision and computer graphics. While various methods have been proposed, they typically do not work well for real-world images due to the presence of rich textures, complex materials, hard shadows, occlusions and color illumination, etc. In this paper, we present a novel specular highlight removal method for real-world images. Our approach is based on two observations of the real-world images: (i) the specular highlight is often small in size and sparse in distribution; (ii) the remaining diffuse image can be represented by linear com- bination of a small number of basis colors with the sparse encoding coefficients. Based on the two observations, we design an optimization framework for simultaneously estimating the diffuse and specular highlight images from a single image. Specif- ically, we recover the diffuse components of those regions with specular highlight by encouraging the encoding coefficients sparseness using L0 norm. Moreover, the encoding coefficients and specular highlight are also subject to the non-negativity according to the additive color mixing theory and the illumination definition, respectively. Extensive experiments have been performed on a variety of images to validate the effectiveness of the proposed method and its superiority over the previous methods.