The approximate GCD of inexact polynomials
ISSAC '04 Proceedings of the 2004 international symposium on Symbolic and algebraic computation
Mumford and Shah Functional: VLSI Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolution enhancement via probabilistic deconvolution of multiple degraded images
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Self-Invertible 2D Log-Gabor Wavelets
International Journal of Computer Vision
Efficient recursive multichannel blind image restoration
EURASIP Journal on Applied Signal Processing
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
Hierarchical Bayesian sparse image reconstruction with application to MRFM
IEEE Transactions on Image Processing
Restoration of color images degraded by space-variant motion blur
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Bayesian blind deconvolution from differently exposed image pairs
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blurred image restoration: A fast method of finding the motion length and angle
Digital Signal Processing
Multichannel image restoration based on optimization of the structural similarity index
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Bayesian blind deconvolution from differently exposed image pairs
IEEE Transactions on Image Processing
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Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately, in a single-channel framework, serious conceptual and numerical problems are often encountered. An eigenvector-based method (EVAM) has been proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied (see Harikumar, G. and Bresler, Y., ibid., vol.8, no.2, p.202-19, 1999; Proc. ICIP 96, vol.3, p.97-100, 1996). We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate the capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.