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Proceedings Paper

Fast stochastic Wiener filter for superresolution image restoration with information theoretic visual quality assessment
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Paper Abstract

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochasticWiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering and image-display processes. We incorporate a new parameter that accounts for LR images registration and fusion errors. Also, we speeded up the performance of the filter by constraining it to work on small patches of the images. Beside this, we introduce two figures of merits: information rate and maximum realizable fidelity, which can be used to assess the visual quality of the resultant images. Simulations and experimental results demonstrate that the derived Wiener filter that can be implemented efficiently in the frequency domain can reduce aliasing, blurring, and noise and result in a sharper reconstructed image. Also, Quantitative assessment using the proposed figures coincides with the visual qualitative assessment. Finally, we evaluate our filter against other SR techniques and its results were very competitive.

Paper Details

Date Published: 7 May 2012
PDF: 14 pages
Proc. SPIE 8399, Visual Information Processing XXI, 839906 (7 May 2012); doi: 10.1117/12.918938
Show Author Affiliations
Amr Hussein Yousef, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)
Mohammad Karim, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 8399:
Visual Information Processing XXI
Mark Allen Neifeld; Amit Ashok, Editor(s)

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