On Interpolation of Differentially Structured Images

Abstract

A vector space approach to image reconstruction is derived and introduced. The continuous-domain image is assumed to belong to a reproducing kernel Hilbert space and the sampling process is shown to correspond to an appropriate orthogonal projection. The values at the interpolating grid are shown to correspond to a set of inner product calculations, giving rise to a minimax solution for an 2approximation problem. A tight upper bound on the ensued error is then derived and demonstrated. Examples of image resizing show that the proposed method yields better results than presently available methods, including the cubic B-spline method, in terms of SNR.

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Document Details

Document Type
Technical Report
Publication Date
Sep 07, 2007
Accession Number
AD1030496

Entities

People

  • Hagai Kirshner
  • Moshe Porat

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Data Processing
  • Diagnostic Imaging
  • Digital Image Processing
  • Digital Images
  • Electrical Engineering
  • Engineering
  • Hilbert Space
  • Image Processing
  • Image Reconstruction
  • Images
  • Interpolation
  • Intervals
  • Polynomials
  • Sampling
  • Signal Processing
  • Two Dimensional

Readers

  • Approximation Theory.
  • Computer Vision.

Technology Areas

  • Space