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
Hauptverfasser: Takeda, H., Farsiu, S., Milanfar, P.
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:
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
scaling
Image processing
irregularly sampled data
spatially adaptive
Noise reduction
Superresolution
nonlinear filter
super-resolution
Non linear filter
local polynomial
Algorithm
nonparametric
Image reconstruction
Kernel method
denoising
fusion
Kernel function
Interpolation
Bilateral filter
Signal processing
kernel regression
Algorithm analysis
Image denoising
ISSN:1057-7149, 1941-0042
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2006.888330