Shape-adaptive DCT for denoising and image reconstruction

Alessandro Foi; Kostadin Dabov; Vladimir Katkovnik; Karen Egiazarian

doi:10.1117/12.642839

17 February 2006 Shape-adaptive DCT for denoising and image reconstruction

Alessandro Foi, Kostadin Dabov, Vladimir Katkovnik, Karen Egiazarian

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 60640N (2006) https://doi.org/10.1117/12.642839
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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.

Citation Download Citation

Alessandro Foi, Kostadin Dabov, Vladimir Katkovnik, and Karen Egiazarian "Shape-adaptive DCT for denoising and image reconstruction", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640N (17 February 2006); https://doi.org/10.1117/12.642839

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