Browsing by Author "Zhu, Lei"

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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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    Non-Local Low-Rank Normal Filtering for Mesh Denoising
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Li, Xianzhi; Zhu, Lei; Fu, Chi-Wing; Heng, Pheng-Ann; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    This paper presents a non-local low-rank normal filtering method for mesh denoising. By exploring the geometric similarity between local surface patches on 3D meshes in the form of normal fields, we devise a low-rank recovery model that filters normal vectors by means of patch groups. In summary, our method has the following key contributions. First, we present the guided normal patch covariance descriptor to analyze the similarity between patches. Second, we pack normal vectors on similar patches into the normal-field patch-group (NPG) matrix for rank analysis. Third, we formulate mesh denoising as a low-rank matrix recovery problem based on the prior that the rank of the NPG matrix is high for raw meshes with noise, but can be significantly reduced for denoised meshes, whose normal vectors across similar patches should be more strongly correlated. Furthermore, we devise an objective function based on an improved truncated 'gamma' norm, and derive an optimization procedure using the alternative direction method of multipliers and iteratively re-weighted least squares techniques.We conducted several experiments to evaluate our method using various 3D models, and compared our results against several state-of-the-art methods. Experimental results show that our method consistently outperforms other methods and better preserves the fine details.