Software
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inertial inexact Proximal algorithm for nonconvex optimization (i2Piano)
and inertial Proximal inexact linesearch algorithm (iPila) |
| Click here to download the Matlab codes for the inertial inexact Proximal algorithm for nonconvex optimization (i2Piano) and inertial Proximal inexact linesearch algortihm (iPila) applied to the edge-preserving deconvolution of images corrupted by impulse noise described in the paper: S. Bonettini, P. Ochs, M. Prato, S. Rebegoldi 2023, An abstract convergence framework with application to inertial inexact forward-backward methods, Computational Optimization and Applications 84(2), 319-362. |
| Scaled Gradient Projection (SGP) method for edge-preserving image deconvolution |
| Click here to download the Matlab codes for the Scaled Gradient Projection (SGP) method applied to the edge-preserving deconvolution of images corrupted by Poisson noise described in the paper: S. Bonettini, F. Porta, V. Ruggiero, L. Zanni 2021, Variable metric techniques for forward-backward methods in imaging, Journal of Computational and Applied Mathematics 385, 113192. |
| Barzilai-Borwein-based steplength rules in Gradient Projection methods for box-constrained quadratic programs |
| Click here to download the Matlab codes for the Gradient Projection methods for box-constrained quadratic programs described in the paper: S. Crisci, V. Ruggiero, L. Zanni 2019, Steplength selection in gradient projection methods for box-constrained quadratic programs. Applied Mathematics and Computation 356, 312-327. |
| Scaled Gradient Projection method for image DEConvolution (SGP_DEC) |
| Click here to download the Interactive Data Language (IDL) code of the Scaled Gradient Projection method for image DEConvolution described in the paper M. Prato, A. La Camera, C. Arcidiacono, P. Boccacci, M. Bertero 2019, Multiple image deblurring with high dynamic-range Poisson data. In: Computational Methods for Inverse Problems in Imaging, M. Donatelli and S. Serra Capizzano (eds.), Springer INdAM Series 36, 117-140. A report on our deconvolution of Keck images of Pillan eruption on Io, UT 2007 August 14, at J, H, K, L and M-band can be downloaded here. We thank Imke de Pater, Berkeley, for permission of using these images. |
| A Parallel Approach for Image Segmentation by Numerical Minimization of a Second-Order Functional |
| Click here to download the C++ code for the minimization of a second-order variational approximation of the Blake-Zissermann functional described in the paper: R. Zanella, F. Porta, V. Ruggiero, M. Zanetti 2018, Serial and parallel approaches for image segmentation by numerical minimization of a second-order functional. Applied Mathematics and Computation 318, 153-175. Additional informations for code compilation and run can be found here. |
| Limited Memory Steepest Descent (LMSD) & Inexact Line–search Algorithm (ILA) for Differential - Interference - Constrast (DIC) microscopy |
| Click here to download the Matlab code of the Limited Memory Steepest Descent (LMSD) and the Inexact Line–search Algorithm (ILA) described in the paper S. Rebegoldi, L. Bautista, L. Blanc-Féraud, M. Prato, L. Zanni, A. Plata 2017, A comparison of edge-preserving approaches for differential interference contrast microscopy. Inverse Problems 33, 085009 to address the problem of estimating the phase of images acquired by differential interference constrast (DIC) microscopes. |
| Variable Metric Inexact Line–search Algorithm (VMILA) |
| Click here to download the Matlab code of the Variable Metric Inexact Line–search Algorithm described in the paper S. Bonettini, I. Loris, F. Porta, M. Prato 2016, Variable metric inexact line-search based methods for nonsmooth optimization, SIAM Journal on Optimization 26(2), 891-921. |
| Scaled Forward-Backward method with Extrapolation (SFBEM) |
| Click here to download the Matlab code of the Scaled Forward-Backward method with Extrapolation (for Poisson noise image deblurring problems) described in the paper S. Bonettini, F. Porta, V. Ruggiero 2016, A variable metric forward-backward method with extrapolation. SIAM Journal on Scientific Computing 38, A2558-A2584. |
| Cyclic scaled gradient projection (CSGP) method |
| The Matlab and IDL routines implementing the blind deconvolution approach for astronomical imaging described in "M. Prato, A. La Camera, S. Bonettini, M. Bertero 2013, A convergent blind deconvolution method for post-adaptive-optics astronomical imaging. Inverse Problems 29, 06517" can be found in the Supplementary Material of the paper. |
More routines of the algorithms recently developed by the members of the group can be found here, including:
- the Alternating Extragradient Method (AEM) Matlab codes to reproduce some of the 2D and 3D numerical experiments in "S. Bonettini, V. Ruggiero 2011, Alternating Extragradient Method for Total Variation image restoration from Poisson data. Inverse Problems 27, 095001"
- the Matlab M-function CLSRit.m described in the paper: "E. Loli Piccolomini, F. Zama 2011, An Iterative algorithm for large size Least-Squares constrained regularization problems. Applied Mathematics and Computations 217, 10343-10354"
- the Scaled Gradient Projection (SGP) Matlab package for the deconvolution of 2D and 3D images corrupted by Poisson noise, as described in "S. Bonettini, R. Zanella, L. Zanni 2009, A scaled gradient projection method for constrained image deblurring. Inverse Problems 25, 015002"
- an Interactive Data Language (IDL) package for the single and multiple deconvolution of 2D images corrupted by Poisson noise, with the optional inclusion of a boundary effect correction, as described in "M. Prato, R. Cavicchioli, L. Zanni, P. Boccacci, M. Bertero 2012, Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes. Astronomy & Astrophysics 539, A133"
| Scaled Gradient Projection (SGP) method for edge-preserving image deconvolution |
| Click here to download the Matlab codes for the Scaled Gradient Projection (SGP) method applied to the edge-preserving deconvolution of images corrupted by Poisson noise described in the paper: S. Bonettini, F. Porta, V. Ruggiero, L. Zanni 2021, Variable metric techniques for forward-backward methods in imaging, Journal of Computational and Applied Mathematics 385, 113192. |
| Scaled Gradient Projection (SGP) method for edge-preserving image deconvolution |
| Click here to download the Matlab codes for the Scaled Gradient Projection (SGP) method applied to the edge-preserving deconvolution of images corrupted by Poisson noise described in the paper: S. Bonettini, F. Porta, V. Ruggiero, L. Zanni 2021, Variable metric techniques for forward-backward methods in imaging, Journal of Computational and Applied Mathematics 385, 113192. |