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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