DocumentCode
1440161
Title
Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation
Author
Lu, Yue M. ; Karzand, Mina ; Vetterli, Martin
Author_Institution
Audiovisual Commun. Lab., Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland
Volume
19
Issue
8
fYear
2010
Firstpage
2085
Lastpage
2098
Abstract
Color image demosaicking is a key process in the digital imaging pipeline. In this paper, we study a well-known and influential demosaicking algorithm based upon alternating projections (AP), proposed by Gunturk, Altunbasak and Mersereau in 2002. Since its publication, the AP algorithm has been widely cited and compared against in a series of more recent papers in the demosaicking literature. Despite good performances, a limitation of the AP algorithm is its high computational complexity. We provide three main contributions in this paper. First, we present a rigorous analysis of the convergence property of the AP demosaicking algorithm, showing that it is a contraction mapping, with a unique fixed point. Second, we show that this fixed point is in fact the solution to a constrained quadratic minimization problem, thus, establishing the optimality of the AP algorithm. Finally, using the tool of polyphase representation, we show how to obtain the results of the AP algorithm in a single step, implemented as linear filtering in the polyphase domain. Replacing the original iterative procedure by the proposed one-step solution leads to substantial computational savings, by about an order of magnitude in our experiments.
Keywords
filtering theory; image colour analysis; image segmentation; minimisation; quadratic programming; alternating projection algorithm; color image demosaicking; computational complexity; constrained quadratic minimization problem; contraction mapping; digital imaging pipeline; linear filtering; Alternating projections; color filter array; contraction mapping; demosaicing; demosaicking; fixed point; multirate signal processing; polyphase representation; projection onto convex sets (POCS); Algorithms; Color; Colorimetry; Image Enhancement; Image Interpretation, Computer-Assisted; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Image Processing, IEEE Transactions on
Publisher
ieee
ISSN
1057-7149
Type
jour
DOI
10.1109/TIP.2010.2045710
Filename
5430905
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