Browsing by Author "Nie, Yongwei"

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    Dual Illumination Estimation for Robust Exposure Correction
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhang, Qing; Nie, Yongwei; Zheng, Wei-Shi; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high-quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under- and over-exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the underand over-exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi-exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well-exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state-of-the-art methods and popular automatic exposure correction tools.
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    GPU-Driven Real-Time Mesh Contour Vectorization
    (The Eurographics Association, 2022) Jiang, Wangziwei; Li, Guiqing; Nie, Yongwei; Xian, Chuhua; Ghosh, Abhijeet; Wei, Li-Yi
    Rendering contours of 3D meshes has a wide range of applications. Previous CPU-based contour rendering algorithms support advanced stylized effects but cannot achieve realtime performance. On the other hand, real-time algorithms based on GPU have to sacrifice some advanced stylization effects due to the difficulty of linking contour elements into stroke curves. This paper proposes a GPU-based mesh contour rendering method which includes the following steps: (1) before rendering, a preprocessing step analyzes the adjacency and geometric information from the 3d mesh model; (2) at runtime, an extraction stage firstly selects contour edges from the 3D mesh model, then the parallelized Bresenham algorithm rasterizes the contour edges into a set of oriented contour pixels; (3) next, Potrace is parallelized to extract (pixel) edge loops from the contour pixels; (4) subsequently, a novel segmentation procedure is designed to partition the edge loops into strokes; (5) finally, these strokes are then converted into 2D strip meshes in order to support rendering with controllable styles. Except the preprocessing step, all other procedures are implemented in parallel on a GPU. This enables our framework to achieve real-time performance for high-resolution rendering of dense mesh models.
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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.