Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid.
The Laplacian pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian pyramid is widely believed to be ill-suited for representing edges, as well as for edge-a...
| Publicado en: | Communications of the ACM Vol. 58; no. 3; pp. 81 - 92 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Association for Computing Machinery
Mar2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=101120137&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 101120137 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Mar2015 vid: 58 iid: 3 pid: 68 pub: Association for Computing Machinery artinfo: ui: 101120137 10.1145/2723694 ppf: 81 ppct: 11 formats: tig: atl: Local Laplacian Filters: Edge-Aware Image Processing with a Laplacian Pyramid. aug: au: Paris, Sylvain Hasinoff, Samuel W. Kautz, Jan affil: Adobe Research Google Inc. University College London su: Image processing software Laplacian operator Edge detection (Image processing) Image enhancement (Imaging systems) Gaussian function Wavelets (Mathematics) Optical resolution Pixels sug: subj: Image processing software Laplacian operator Edge detection (Image processing) Image enhancement (Imaging systems) Gaussian function Wavelets (Mathematics) Optical resolution Pixels ab: The Laplacian pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian pyramid is widely believed to be ill-suited for representing edges, as well as for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, for example, anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to postprocess the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian pyramids. We characterize edges with a simple threshold on pixel values that allow us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or postprocessing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2015 holdings: @attributes: islocal: N |
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