Fundamentals of digital image processing
Fundamentals of digital image processing
Digital image processing
Wavelets and curvelets for image deconvolution: a combined approach
Signal Processing - Special section: Security of data hiding technologies
Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
SIAM Journal on Imaging Sciences
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
IEEE Transactions on Image Processing
Iterative Wiener filters for image restoration
IEEE Transactions on Signal Processing
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Deconvolution by thresholding in mirror wavelet bases
IEEE Transactions on Image Processing
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
A spatially adaptive nonparametric regression image deblurring
IEEE Transactions on Image Processing
Texas Two-Step: A Framework for Optimal Multi-Input Single-Output Deconvolution
IEEE Transactions on Image Processing
A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution
IEEE Transactions on Image Processing
Deblurring Using Regularized Locally Adaptive Kernel Regression
IEEE Transactions on Image Processing
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In this article, we propose a new deconvolution algorithm, which is based on image reconstruction from incomplete measurements in Fourier domain. Our algorithm has two steps. First, an initial estimator is obtained using Fourier regularized inverse operator. Second, parts of the estimator's Fourier coefficients are saved, and the others are removed to suppress noise energy, then the remaining coefficients are used to recover image based on the sparse constraints. This image reconstruction problem is an optimization problem that is solved by a fast algorithm named split Bregman iteration. Different from other deconvolution algorithms, our algorithm only uses parts of Fourier components to restore the blurred image and combines two different regularization strategies efficiently by applying a selection matrix. The experiment shows that our method gives better performance than many other competitive deconvolution methods. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 233–240, 2012 © 2012 Wiley Periodicals, Inc.