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Proceedings Paper

Multichannel image deblurring of raw color components
Author(s): Mejdi Trimeche; Dmitry Paliy; Markku Vehvilainen; Vladimir Katkovnic
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Paper Abstract

This paper presents a novel multi-channel image restoration algorithm. The main idea is to develop practical approaches to reduce optical blur from noisy observations produced by the sensor of a camera phone. An iterative deconvolution is applied separately to each color channel directly on the raw data obtained from the camera sensor. We use a modified iterative Landweber algorithm combined with an adaptive denoising technique. The employed adaptive denoising is based on Local Polynomial Approximation (LPA) operating on data windows, which are selected by the rule of Intersection of Confidence Intervals (ICI). In order to avoid false coloring due to independent component filtering in RGB space, we have integrated a novel regularization mechanism that smoothly attenuates the high-pass filtering near saturated regions. Through simulations, it is shown that the proposed filtering is robust with respect to errors in point-spread function (PSF) and approximated noise models. Experimental results show that the proposed processing technique produces significant improvement in perceived image resolution.

Paper Details

Date Published: 11 March 2005
PDF: 10 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.586598
Show Author Affiliations
Mejdi Trimeche, Nokia Research Ctr. (Finland)
Dmitry Paliy, Tampere Univ. of Technology (Finland)
Markku Vehvilainen, Nokia Research Ctr. (Finland)
Vladimir Katkovnic, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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