Enhanced Biggs---Andrews Asymmetric Iterative Blind Deconvolution
Multidimensional Systems and Signal Processing
Resolution enhancement via probabilistic deconvolution of multiple degraded images
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Blind calibration of sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Efficient recursive multichannel blind image restoration
EURASIP Journal on Applied Signal Processing
Blind image deblurring driven by nonlinear processing in the edge domain
EURASIP Journal on Applied Signal Processing
MCA: a multichannel approach to SAR autofocus
IEEE Transactions on Image Processing
A new look to multichannel blind image deconvolution
IEEE Transactions on Image Processing
Generic invertibility of multidimensional FIR filter banks and MIMO systems
IEEE Transactions on Signal Processing
Single channel 2-D and 3-D blind image deconvolution for circularly symmetric fir blurs
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image reconstruction from phased-array MRI data based on multichannel blind deconvolution
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A study on new right/left inverses of nonsquare polynomial matrices
International Journal of Applied Mathematics and Computer Science - SPECIAL SECTION: Efficient Resource Management for Grid-Enabled Applications
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
Deconvolving PSFs for a better motion deblurring using multiple images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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We address the problem of restoring an image from its noisy convolutions with two or more unknown finite impulse response (FIR) filters. We develop theoretical results about the existence and uniqueness of solutions, and show that under some generically true assumptions, both the filters and the image can be determined exactly in the absence of noise, and stably estimated in its presence. We present efficient algorithms to estimate the blur functions and their sizes. These algorithms are of two types, subspace-based and likelihood-based, and are extensions of techniques proposed for the solution of the multichannel blind deconvolution problem in one dimension. We present memory and computation-efficient techniques to handle the very large matrices arising in the two-dimensional (2-D) case. Once the blur functions are determined, they are used in a multichannel deconvolution step to reconstruct the unknown image. The theoretical and practical implications of edge effects, and “weakly exciting” images are examined. Finally, the algorithms are demonstrated on synthetic and real data