Kernel Regression for Image Processing and Reconstruction
In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation,...
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| Veröffentlicht in: | IEEE transactions on image processing Jg. 16; H. 2; S. 349 - 366 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
IEEE
01.02.2007
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1057-7149, 1941-0042 |
| Online-Zugang: | Volltext |
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| Abstract | In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples |
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| AbstractList | In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples. In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples. |
| Author | Farsiu, S. Takeda, H. Milanfar, P. |
| Author_xml | – sequence: 1 givenname: H. surname: Takeda fullname: Takeda, H. organization: Electr. Eng. Dept., Univ. of California, Santa Cruz, CA – sequence: 2 givenname: S. surname: Farsiu fullname: Farsiu, S. organization: Electr. Eng. Dept., Univ. of California, Santa Cruz, CA – sequence: 3 givenname: P. surname: Milanfar fullname: Milanfar, P. organization: Electr. Eng. Dept., Univ. of California, Santa Cruz, CA |
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| CODEN | IIPRE4 |
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| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Bilateral filter Charge coupled devices Contact Costs denoising Detection, estimation, filtering, equalization, prediction Digital images Exact sciences and technology Filters fusion Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image reconstruction Information Storage and Retrieval - methods Information, signal and communications theory Interpolation irregularly sampled data Kernel kernel function kernel regression Kernels local polynomial Miscellaneous Noise reduction nonlinear filter nonparametric Reconstruction Regression Regression Analysis scaling Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal, noise Spatial resolution spatially adaptive super-resolution Telecommunications and information theory |
| Title | Kernel Regression for Image Processing and Reconstruction |
| URI | https://ieeexplore.ieee.org/document/4060955 https://www.ncbi.nlm.nih.gov/pubmed/17269630 https://www.proquest.com/docview/865030709 https://www.proquest.com/docview/68963530 https://www.proquest.com/docview/896195991 |
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