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Proceedings Paper

Shape-adaptive DCT for denoising and image reconstruction
Author(s): Alessandro Foi; Kostadin Dabov; Vladimir Katkovnik; Karen Egiazarian
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Paper Abstract

The shape-adaptive DCT (SA-DCT) can be computed on a support of arbitrary shape, but retains a computational complexity comparable to that of the usual separable block DCT. Despite the near-optimal decorrelation and energy compaction properties, application of the SA-DCT has been rather limited, targeted nearly exclusively to video compression. It has been recently proposed by the authors8 to employ the SA-DCT for still image denoising. We use the SA-DCT in conjunction with the directional LPA-ICI technique, which defines the shape of the transform's support in a pointwise adaptive manner. The thresholded or modified SA-DCT coefficients are used to reconstruct a local estimate of the signal within the adaptive-shape support. Since supports corresponding to different points are in general overlapping, the local estimates are averaged together using adaptive weights that depend on the region's statistics. In this paper we further develop this novel approach and extend it to more general restoration problems, with particular emphasis on image deconvolution. Simulation experiments show a state-of-the-art quality of the final estimate, both in terms of objective criteria and visual appearance. Thanks to the adaptive support, reconstructed edges are clean, and no unpleasant ringing artifacts are introduced by the fitted transform.

Paper Details

Date Published: 17 February 2006
PDF: 12 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640N (17 February 2006); doi: 10.1117/12.642839
Show Author Affiliations
Alessandro Foi, Tampere Univ. of Technology (Finland)
Kostadin Dabov, Tampere Univ. of Technology (Finland)
Vladimir Katkovnik, Tampere Univ. of Technology (Finland)
Karen Egiazarian, Tampere Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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