Gradient domain high dynamic range compression
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes
IEEE Transactions on Visualization and Computer Graphics
ACM SIGGRAPH 2003 Papers
The trilateral filter for high contrast images and meshes
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Numerical computation of rectangular bivariate and trivariate normal and t probabilities
Statistics and Computing
Digital photography with flash and no-flash image pairs
ACM SIGGRAPH 2004 Papers
Flash photography enhancement via intrinsic relighting
ACM SIGGRAPH 2004 Papers
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Real-time edge-aware image processing with the bilateral grid
ACM SIGGRAPH 2007 papers
Gaussian KD-trees for fast high-dimensional filtering
ACM SIGGRAPH 2009 papers
A fast approximation of the bilateral filter using a signal processing approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Fast anisotropic Gauss filtering
IEEE Transactions on Image Processing
An Optimal Nonorthogonal Separation of the Anisotropic Gaussian Convolution Filter
IEEE Transactions on Image Processing
Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
IEEE Transactions on Image Processing
Image smoothing via L0 gradient minimization
Proceedings of the 2011 SIGGRAPH Asia Conference
Structure-preserving image smoothing via region covariances
ACM Transactions on Graphics (TOG)
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High-dimensional Gaussian filters, most notably the bilateral filter, are important tools for many computer graphics and vision tasks. In recent years, a number of techniques for accelerating their evaluation have been developed by exploiting the separability of these Gaussians. However, these techniques do not apply to the more general class of spatially varying Gaussian filters, as they cannot be expressed as convolutions. These filters are useful because the underlying data---e.g. images, range data, meshes or light fields---often exhibit strong local anisotropy and scale. We propose an acceleration method for approximating spatially varying Gaussian filters using a set of spatially invariant Gaussian filters each of which is applied to a segment of some non-disjoint partitioning of the dataset. We then demonstrate that the resulting ability to locally tilt, rotate or scale the kernel improves filtering performance in various applications over traditional spatially invariant Gaussian filters, without incurring a significant penalty in computational expense.