Edge-preserving Multiscale Image Decomposition based on Local Extrema
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 osci...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2012
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| Online Access: | http://hdl.handle.net/1721.1/72593 https://orcid.org/0000-0001-9919-069X |
| _version_ | 1826191575153115136 |
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| author | Subr, Kartic Soler, Cyril Durand, Fredo |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Subr, Kartic Soler, Cyril Durand, Fredo |
| author_sort | Subr, Kartic |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:58:22Z |
| format | Article |
| id | mit-1721.1/72593 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:58:22Z |
| publishDate | 2012 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/725932022-09-30T12:29:00Z Edge-preserving Multiscale Image Decomposition based on Local Extrema Subr, Kartic Soler, Cyril Durand, Fredo Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Durand, Fredo Durand, Fredo 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. 2012-09-10T15:33:50Z 2012-09-10T15:33:50Z 2009 Article http://purl.org/eprint/type/JournalArticle 978-1-60558-858-2 http://hdl.handle.net/1721.1/72593 Kartic Subr, Cyril Soler, and Frédo Durand. 2009. Edge-preserving multiscale image decomposition based on local extrema. ACM SIGGRAPH Asia 2009 papers (SIGGRAPH Asia '09). ACM, New York, NY, USA, , Article 147 , 9 pages. https://orcid.org/0000-0001-9919-069X en_US http://dx.doi.org/10.1145/1618452.1618493 ACM SIGGRAPH Asia 2009 papers (SIGGRAPH Asia '09) Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Association for Computing Machinery (ACM) Other University Web Domain |
| spellingShingle | Subr, Kartic Soler, Cyril Durand, Fredo Edge-preserving Multiscale Image Decomposition based on Local Extrema |
| title | Edge-preserving Multiscale Image Decomposition based on Local Extrema |
| title_full | Edge-preserving Multiscale Image Decomposition based on Local Extrema |
| title_fullStr | Edge-preserving Multiscale Image Decomposition based on Local Extrema |
| title_full_unstemmed | Edge-preserving Multiscale Image Decomposition based on Local Extrema |
| title_short | Edge-preserving Multiscale Image Decomposition based on Local Extrema |
| title_sort | edge preserving multiscale image decomposition based on local extrema |
| url | http://hdl.handle.net/1721.1/72593 https://orcid.org/0000-0001-9919-069X |
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