Fundamentals of digital image processing
Fundamentals of digital image processing
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Multi-channel restoration of electron micrographs
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Multichannel Blind Deconvolution of the Short-Exposure Astronomical Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Joint order detection and blind channel estimation by least squaressmoothing
IEEE Transactions on Signal Processing
Blind system identification using minimum noise subspace
IEEE Transactions on Signal Processing
Generalized multichannel image-filtering structures
IEEE Transactions on Image Processing
Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms
IEEE Transactions on Image Processing
Exact image deconvolution from multiple FIR blurs
IEEE Transactions on Image Processing
An enhanced NAS-RIF algorithm for blind image deconvolution
IEEE Transactions on Image Processing
Regularization of RIF blind image deconvolution
IEEE Transactions on Image Processing
Blind identification of multichannel FIR blurs and perfect image restoration
IEEE Transactions on Image Processing
Multichannel blind iterative image restoration
IEEE Transactions on Image Processing
Multichannel blind deconvolution of spatially misaligned images
IEEE Transactions on Image Processing
No-reference blur image quality measure based on multiplicative multiresolution decomposition
Journal of Visual Communication and Image Representation
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The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to estimate the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.