Resolution enhancement via probabilistic deconvolution of multiple degraded images
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Restoring images with a multiscale neural network based technique
Proceedings of the 2008 ACM symposium on Applied computing
A soft MAP framework for blind super-resolution image reconstruction
Image and Vision Computing
A new look to multichannel blind image deconvolution
IEEE Transactions on Image Processing
Bayesian blind deconvolution from differently exposed image pairs
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Space-variant deblurring using one blurred and one underexposed image
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Bayesian blind deconvolution from differently exposed image pairs
IEEE Transactions on Image Processing
Motion blur concealment of digital video using invariant features
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Multichannel blind deconvolution in eye fundus imaging
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
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Existing multichannel blind restoration techniques assume perfect spatial alignment of channels, correct estimation of blur size, and are prone to noise. We developed an alternating minimization scheme based on a maximum a posteriori estimation with a priori distribution of blurs derived from the multichannel framework and a priori distribution of original images defined by the variational integral. This stochastic approach enables us to recover the blurs and the original image from channels severely corrupted by noise. We observe that the exact knowledge of the blur size is not necessary, and we prove that translation misregistration up to a certain extent can be automatically removed in the restoration process.