An overview of morphological filtering
Circuits, Systems, and Signal Processing - Special issue: median and morphological filters
LCIS: a boundary hierarchy for detail-preserving contrast reduction
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Image-based modeling and photo editing
Proceedings of the 28th 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
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Two-scale tone management for photographic look
ACM SIGGRAPH 2006 Papers
Interactive local adjustment of tonal values
ACM SIGGRAPH 2006 Papers
The trilateral filter for high contrast images and meshes
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Multiscale shape and detail enhancement from multi-light image collections
ACM SIGGRAPH 2007 papers
Real-time edge-aware image processing with the bilateral grid
ACM SIGGRAPH 2007 papers
Edge-preserving decompositions for multi-scale tone and detail manipulation
ACM SIGGRAPH 2008 papers
The study of the intermittency test filtering character of Hilbert-Huang transform
Mathematics and Computers in Simulation
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)
Smoothed local histogram filters
ACM SIGGRAPH 2010 papers
Diffusion maps for edge-aware image editing
ACM SIGGRAPH Asia 2010 papers
Image and video decolorization by fusion
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Local Laplacian filters: edge-aware image processing with a Laplacian pyramid
ACM SIGGRAPH 2011 papers
Domain transform for edge-aware image and video processing
ACM SIGGRAPH 2011 papers
Displacement interpolation using Lagrangian mass transport
Proceedings of the 2011 SIGGRAPH Asia Conference
Image smoothing via L0 gradient minimization
Proceedings of the 2011 SIGGRAPH Asia Conference
Local oscillation suppression based on joint bilateral filtering framework
SIGGRAPH Asia 2011 Posters
A local variance-based bilateral filtering for artifact-free detail- and edge-preserving smoothing
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Empirical mode decomposition on surfaces
Graphical Models
Structure extraction from texture via relative total variation
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Digital reconstruction of halftoned color comics
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Edge-preserving image decomposition using L1 fidelity with L0 gradient
SIGGRAPH Asia 2012 Technical Briefs
Structure-preserving image smoothing via region covariances
ACM Transactions on Graphics (TOG)
A sparse control model for image and video editing
ACM Transactions on Graphics (TOG)
Image coarsening by using space-filling curve for decomposition-based image enhancement
Journal of Visual Communication and Image Representation
Fast multi-scale detail decomposition via accelerated iterative shrinkage
SIGGRAPH Asia 2013 Technical Briefs
CAD/Graphics 2013: Feature-preserving filtering with L0 gradient minimization
Computers and Graphics
Oscillation analysis for salient object detection
Multimedia Tools and Applications
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We propose a new model for detail that inherently captures oscillations, a key property that distinguishes textures from individual edges. Inspired by techniques in empirical data analysis and morphological image analysis, we use the local extrema of the input image to extract information about oscillations: We define detail as oscillations between local minima and maxima. Building on the key observation that the spatial scale of oscillations are characterized by the density of local extrema, we develop an algorithm for decomposing images into multiple scales of superposed oscillations. Current edge-preserving image decompositions assume image detail to be low contrast variation. Consequently they apply filters that extract features with increasing contrast as successive layers of detail. As a result, they are unable to distinguish between high-contrast, fine-scale features and edges of similar contrast that are to be preserved. We compare our results with existing edge-preserving image decomposition algorithms and demonstrate exciting applications that are made possible by our new notion of detail.