| Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems JM Bardsley, A Solonen, H Haario, M Laine SIAM Journal on Scientific Computing 36 (4), A1895-A1910, 2014 | 147 | 2014 |
| MCMC-BASED IMAGE RECONSTRUCTION WITH UNCERTAINTY QUANTIFICATION JM BARDSLEY SIAM Journal on Scientific Computing 34 (3), A1316–A1332, 2012 | 123 | 2012 |
| Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation JM Bardsley, J Goldes Inverse Problems 25 (9), 095005, 2009 | 122 | 2009 |
| A nonnegatively constrained convex programming method for image reconstruction JM Bardsley, CR Vogel SIAM Journal on Scientific Computing 25 (4), 1326-1343, 2004 | 107 | 2004 |
| Total variation-penalized Poisson likelihood estimation for ill-posed problems JM Bardsley, A Luttman Advances in Computational Mathematics 31 (1), 35-59, 2009 | 99 | 2009 |
| Covariance-preconditioned iterative methods for nonnegatively constrained astronomical imaging JM Bardsley, JG Nagy SIAM journal on matrix analysis and applications 27 (4), 1184-1197, 2006 | 93 | 2006 |
| A computational method for the restoration of images with an unknown, spatially-varying blur J Bardsley, S Jefferies, J Nagy, R Plemmons Optics express 14 (5), 1767-1782, 2006 | 85 | 2006 |
| Computational Uncertainty Quantification for Inverse Problems JM Bardsley Society for Industrial and Applied Mathematics, 2018 | 82 | 2018 |
| Analysis of the Gibbs sampler for hierarchical inverse problems S Agapiou, JM Bardsley, O Papaspiliopoulos, AM Stuart SIAM/ASA Journal on Uncertainty Quantification 2 (1), 511-544, 2014 | 61 | 2014 |
| Wavefront reconstruction methods for adaptive optics systems on ground-based telescopes JM Bardsley SIAM Journal on Matrix Analysis and Applications 30 (1), 67-83, 2008 | 47 | 2008 |
| Tikhonov regularized Poisson likelihood estimation: theoretical justification and a computational method JM Bardsley, N Laobeul Inverse Problems in Science and Engineering 16 (2), 199-215, 2008 | 46 | 2008 |
| The variational Kalman filter and an efficient implementation using limited memory BFGS H Auvinen, JM Bardsley, H Haario, T Kauranne International Journal for Numerical Methods in Fluids 64 (3), 314-335, 2010 | 45 | 2010 |
| Least-squares methods with Poissonian noise: Analysis and comparison with the Richardson-Lucy algorithm R Vio, J Bardsley, W Wamsteker Astronomy & Astrophysics 436 (2), 741-755, 2005 | 45 | 2005 |
| GAUSSIAN MARKOV RANDOM FIELD PRIORS FOR INVERSE PROBLEMS JM Bardsley Inverse Problems and Imaging 7 (2), 397 - 416, 2013 | 42 | 2013 |
| An efficient computational method for total variation-penalized Poisson likelihood estimation JM Bardsley Inverse Problems and Imaging 2 (2), 167-185, 2008 | 42 | 2008 |
| Bayesian Inverse Problems with Priors: A Randomize-Then-Optimize Approach Z Wang, JM Bardsley, A Solonen, T Cui, YM Marzouk SIAM Journal on Scientific Computing 39 (5), S140-S166, 2017 | 41 | 2017 |
| Hierarchical regularization for edge-preserving reconstruction of PET images JM Bardsley, D Calvetti, E Somersalo Inverse Problems 26 (3), 035010, 2010 | 40 | 2010 |
| Structured linear algebra problems in adaptive optics imaging JM Bardsley, S Knepper, J Nagy Advances in Computational Mathematics 35 (2), 103-117, 2011 | 32 | 2011 |
| Computational methods for a large-scale inverse problem arising in atmospheric optics L Gilles, CR Vogel, JM Bardsley Inverse Problems 18 (1), 237, 2002 | 31 | 2002 |
| Dealing with edge effects in least-squares image deconvolution problems R Vio, J Bardsley, M Donatelli, W Wamsteker Astronomy & Astrophysics 442 (1), 397-403, 2005 | 30 | 2005 |
heikki haarioprofessor, LUT在 lut.fi 的电子邮件经过验证
