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Proceedings Paper

Spatially adaptive regularized iterative high-resolution image reconstruction algorithm
Author(s): Won Bae Lim; Min Kyu Park; Moon Gi Kang
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Paper Abstract

High resolution images are often required in applications such as remote sensing, frame freeze in video, military and medical imaging. Digital image sensor arrays, which are used for image acquisition in many imaging systems, are not dense enough to prevent aliasing, so the acquired images will be degraded by aliasing effects. To prevent aliasing without loss of resolution, a dense detector array is required. But it may be very costly or unavailable, thus, many imaging systems are designed to allow some level of aliasing during image acquisition. The purpose of our work is to reconstruct an unaliased high resolution image from the acquired aliased image sequence. In this paper, we propose a spatially adaptive regularized iterative high resolution image reconstruction algorithm for blurred, noisy and down-sampled image sequences. The proposed approach is based on a Constrained Least Squares (CLS) high resolution reconstruction algorithm, with spatially adaptive regularization operators and parameters. These regularization terms are shown to improve the reconstructed image quality by forcing smoothness, while preserving edges in the reconstructed high resolution image. Accurate sub-pixel motion registration is the key of the success of the high resolution image reconstruction algorithm. However, sub-pixel motion registration may have some level of registration error. Therefore, a reconstruction algorithm which is robust against the registration error is required. The registration algorithm uses a gradient based sub-pixel motion estimator which provides shift information for each of the recorded frames. The proposed algorithm is based on a technique of high resolution image reconstruction, and it solves spatially adaptive regularized constrained least square minimization functionals. In this paper, we show that the reconstruction algorithm gives dramatic improvements in the resolution of the reconstructed image and is effective in handling the aliased information. The proposed algorithm is also shown to be robust in the presence of severe registration error. Experimental results are provided to illustrate the performance of the proposed reconstruction algorithm. Comparative analyses with other reconstruction methods are also provided.

Paper Details

Date Published: 29 December 2000
PDF: 11 pages
Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); doi: 10.1117/12.411797
Show Author Affiliations
Won Bae Lim, Yonsei Univ. (South Korea)
Min Kyu Park, Yonsei Univ. (South Korea)
Moon Gi Kang, Yonsei Univ. (South Korea)


Published in SPIE Proceedings Vol. 4310:
Visual Communications and Image Processing 2001
Bernd Girod; Charles A. Bouman; Eckehard G. Steinbach, Editor(s)

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