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
Recovery of blocky images from noisy and blurred data
SIAM Journal on Applied Mathematics
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming
Numerische Mathematik
Covariance-Preconditioned Iterative Methods for Nonnegatively Constrained Astronomical Imaging
SIAM Journal on Matrix Analysis and Applications
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
IEEE Transactions on Image Processing
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
A Primal-Dual Active-Set Method for Non-Negativity Constrained Total Variation Deblurring Problems
IEEE Transactions on Image Processing
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Several image restoration applications require the solution of nonnegatively constrained minimization problems whose objective function is typically constituted by the sum of a data fit function and a regularization function. Newton projection methods are very attractive because of their fast convergence, but they need an efficient implementation to avoid time consuming iterations. In this paper we present NPTool, a set of Matlab functions implementing Newton projection methods for image denoising and deblurring applications. They are specifically thought for two different data fit functions, the Least Squares function and the Kullback---Leibler divergence, and two regularization functions, Tikhonov and Total Variation, giving the opportunity of solving a large variety of restoration problems. The package is easily extensible to other linear or nonlinear data fit and regularization functions. Some examples of its use are included in the package and shown in this paper.