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  • Research Article
  • Open Access

Improving a Single Down-Sampled Image Using Probability-Filtering-Based Interpolation and Improved Poisson Maximum A Posteriori Super-Resolution

EURASIP Journal on Advances in Signal Processing20062006:097492

https://doi.org/10.1155/ASP/2006/97492

  • Received: 29 November 2004
  • Accepted: 4 May 2005
  • Published:

Abstract

We present a novel hybrid scheme called "hyper-resolution" that integrates image probability-filtering-based interpolation and improved Poisson maximum a posteriori (MAP) super-resolution to respectively enhance high spatial and spatial-frequency resolutions of a single down-sampled image. A new approach to interpolation is proposed for simultaneous image interpolation and smoothing by exploiting the probability filter coupled with a pyramidal decomposition and the Poisson MAP super-resolution is improved with the techniques of edge maps and pseudo-blurring. Simulation results demonstrate that this hyper-resolution scheme substantially improves the quality of a single gray-level, color, or noisy image, respectively.

Keywords

  • Color
  • Information Technology
  • Quantum Information
  • Noisy Image
  • Hybrid Scheme

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Authors’ Affiliations

(1)
Department of Computer Science and Information Engineering, Tung-Nan Institute of Technology, Shenkeng, Taipei County, 222, Taiwan

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