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Modified filtered importance sampling for virtual spherical Gaussian lights

computational Visual Media 2016年第4期2卷 343-355页

作者： Yusuke Tokuyoshi Square Enix Co. Ltd.

This paper proposes a modification of the filtered importance sampling method, and improves the quality of virtual spherical Gaussian light(VSGL)-based real-time glossy indirect illumination using this modification. The original filtered importance sampling method produces large overlaps of and gaps between filtering kernels for high-frequency probability density functions(PDFs). This is because the size of the filtering kernel is determined using the PDF at the sampled center of the kernel. To reduce those overlaps and gaps, this paper determines the kernel size using the integral of the PDF within the filtering kernel. Our key insight is that these integrals are approximately constant, if kernel centers are sampled using stratified sampling. Therefore, an appropriate kernel size can be obtained by solving this integral equation. Using the proposed kernel size for filtered importance samplingbased VSGL generation, undesirable artifacts are significantly reduced with a negligibly small overhead.

关键词： filtered importance sampling real-time rendering global illumination virtual point lights

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Removing mixed noise in low rank textures by convex optimization

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computational Visual Media 2016年第3期2卷 267-276页

作者： Xiao Liang Institute for Advanced Study Tsinghua University

This paper introduces a new low rank texture image denoising algorithm, which can restore low rank texture contaminated by both Gaussian and salt-and-pepper noise. The algorithm formulates texture image denoising in terms of solving a low rank matrix optimization problem. Simply assuming low rank is insufficient to describe the properties of natural images, causing high noise amplitudes which lead to unsatisfactory denoising results or serious loss of image details. Thus, in addition to the low rank assumption,the continuity of natural images is also assumed by the algorithm, by adding a total variation regularizer to the optimization objective function. We further give an effective algorithm to solve this optimization problem. By combining the low rank and continuity assumptions, the proposed algorithm overcomes the deficiencies of using either the low rank assumption or total variation regularization alone. Experiments show that our algorithm can effectively remove mixed noise in low rank texture images, and is better than existing algorithms in both its subjective visual effects and in terms of quantitative objective measures.

关键词： image denoising low rank texture total variation convex optimization augmented Lagrangian method

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