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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 Processing volume 2006, Article number: 097492 (2006) Cite this article

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.

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

    Min-Cheng Pan

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Correspondence to Min-Cheng Pan.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pan, MC. Improving a Single Down-Sampled Image Using Probability-Filtering-Based Interpolation and Improved Poisson Maximum A Posteriori Super-Resolution. EURASIP J. Adv. Signal Process. 2006, 097492 (2006). https://doi.org/10.1155/ASP/2006/97492

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

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