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)
Efficient recursive multichannel blind image restoration
EURASIP Journal on Applied Signal Processing
Iterative desensitisation of image restoration filters under wrong PSF and noise estimates
EURASIP Journal on Applied Signal Processing
Blind image deblurring driven by nonlinear processing in the edge domain
EURASIP Journal on Applied Signal Processing
Multiple-Image-Based Restoration for Motion Blur with Non-uniform Point Spread Function
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A new look to multichannel blind image deconvolution
IEEE Transactions on Image Processing
Blind image deconvolution via fast approximate GCD
Proceedings of the 2010 International Symposium on Symbolic and Algebraic Computation
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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Despite its practical importance in image processing and computer vision, blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero or minimum-phase point-spread functions. Relying upon diversity (availability of a sufficient number of multiple blurred images), we develop blind FIR blur identification and order determination schemes. Apart from a minimal persistence of the excitation condition (also present with nonblind setups), the inaccessible input image is allowed to be deterministic or random and of unknown color of distribution. With the blurs satisfying a certain co-primeness condition in addition, we establish existence and uniqueness results which guarantee that single input/multiple-output FIR blurred images can be restored blindly, though perfectly in the absence of noise, using linear FIR filters. Results of simulations employing the blind order determination, blind blur identification, and blind image restoration algorithms are presented. When the SNR is high, direct image restoration is found to yield better results than indirect image restoration which employs the estimated blurs. In low SNR, indirect image restoration performs well while the direct restoration results vary with the delay but improve with larger equalizer orders