Multirate systems and filter banks
Multirate systems and filter banks
Wavelets and subband coding
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Two-Dimensional Digital Filters
Two-Dimensional Digital Filters
Practical implementation of LMMSE demosaicing using luminance and chrominance spaces
Computer Vision and Image Understanding
Spatially adaptive color filter array interpolation for noiseless and noisy data: Articles
International Journal of Imaging Systems and Technology - Special Issue on Applied Color Image Processing
Regularization approaches to demosaicking
IEEE Transactions on Image Processing
A generic variational approach for demosaicking from an arbitrary color filter array
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Color plane interpolation using alternating projections
IEEE Transactions on Image Processing
Primary-consistent soft-decision color demosaicking for digital cameras (patent pending)
IEEE Transactions on Image Processing
Adaptive homogeneity-directed demosaicing algorithm
IEEE Transactions on Image Processing
Demosaicing by successive approximation
IEEE Transactions on Image Processing
Linear demosaicing inspired by the human visual system
IEEE Transactions on Image Processing
Color demosaicking via directional linear minimum mean square-error estimation
IEEE Transactions on Image Processing
Adaptive Filtering for Color Filter Array Demosaicking
IEEE Transactions on Image Processing
Demosaicked image postprocessing using local color ratios
IEEE Transactions on Circuits and Systems for Video Technology
Color image demosaicking: An overview
Image Communication
Computational Optimization and Applications
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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.