generated by bibbase.org
  2026 (4)
ForcingDAS: unified and robust data assimilation via diffusion forcing. Jia, Y.; Chen, S.; Pan, Y.; Li, X.; Shi, L.; Jung, C.; Yuan, H.; Alkhouri, I.; Wu, Y. C.; Ravishankar, S.; Fessler, J. A.; and Qu, Q. 2026.
ForcingDAS: unified and robust data assimilation via diffusion forcing [link]Paper   link   bibtex  
MCLR: improving conditional modeling in visual generative models via inter-class likelihood-ratio maximization and establishing the equivalence between classifier-free guidance and alignment objectives. Li, X.; Jia, Y.; Li, X.; Fessler, J. A.; Wang, R.; and Qu, Q. 2026.
MCLR: improving conditional modeling in visual generative models via inter-class likelihood-ratio maximization and establishing the equivalence between classifier-free guidance and alignment objectives [link]Paper   link   bibtex  
Comparing implicit neural representations and B-splines for continuous function fitting from sparse samples. Yu, H.; Jiang, Y.; and Fessler, J. A. 2026.
Comparing implicit neural representations and B-splines for continuous function fitting from sparse samples [link]Paper   link   bibtex  
Scan-adaptive dynamic MRI undersampling using a dictionary of efficiently learned patterns. Gautam, S.; Li, A.; Agarwal, P. P.; Attili, A. K.; Fessler, J. A.; Seiberlich, N.; and Ravishankar, S. 2026.
Scan-adaptive dynamic MRI undersampling using a dictionary of efficiently learned patterns [link]Paper   link   bibtex  
  2025 (10)
South-American adventures from kayak to inverse problems: A tribute to Alvaro Rodolfo De Pierro. Iusem, A.; Furuie, S.; Helou, E. S.; Miqueles, E. X.; Fessler, J. A.; Zibetti, M. V. W.; Leitao, A.; Martinez, A. G.; Luke, R.; Katsekpor, T.; and Martinez, J. M. 2025.
link   bibtex  
Local patches meet global context: Scalable 3D diffusion priors for CT reconstruction. Yang, T.; Hu, J.; Fessler, J. A.; and Shen, L. 2025.
Local patches meet global context: Scalable 3D diffusion priors for CT reconstruction [link]Paper   link   bibtex  
A convergent generalized Krylov subspace method for compressed sensing MRI reconstruction with gradient-driven denoisers. Hong, T.; Villa, U.; and Fessler, J. A. 2025.
A convergent generalized Krylov subspace method for compressed sensing MRI reconstruction with gradient-driven denoisers [link]Paper   link   bibtex  
Spatiotemporal maps for dynamic MRI reconstruction. Lobos, R. A.; Wang, X.; Fung, R. T. L.; He, Y.; Frey, D.; Gupta, D.; Liu, Z.; Fessler, J. A.; and Noll, D. C. 2025.
Spatiotemporal maps for dynamic MRI reconstruction [link]Paper   link   bibtex  
ALPCAHUS: subspace clustering for heteroscedastic data. Cavazos, J. A. S.; Fessler, J. A.; and Balzano, L. 2025.
ALPCAHUS: subspace clustering for heteroscedastic data [link]Paper   link   bibtex  
Smooth optimization algorithms for global and locally low-rank regularizers. Lobos, R. A.; Cavazos, J. S.; Nadakuditi, R. R.; and Fessler, J. A. 2025.
Smooth optimization algorithms for global and locally low-rank regularizers [link]Paper   link   bibtex  
Convergent complex quasi-Newton proximal method for gradient-driven denoisers in compressed sensing MRI reconstruction. Hong, T.; Xu, Z.; Chun, S. Y.; Hernandez-Garcia, L.; and Fessler, J. A. 2025.
Convergent complex quasi-Newton proximal method for gradient-driven denoisers in compressed sensing MRI reconstruction [link]Paper   link   bibtex  
Bilevel optimized implicit neural representation for scan-specific accelerated MRI reconstruction. Yu, H.; Fessler, J. A.; and Jiang, Y. 2025.
Bilevel optimized implicit neural representation for scan-specific accelerated MRI reconstruction [link]Paper   link   bibtex  
Scan-adaptive MRI undersampling using neighbor-based optimization (SUNO). Gautam, S.; Li, A.; Seiberlich, N.; Fessler, J. A.; and Ravishankar, S. 2025.
Scan-adaptive MRI undersampling using neighbor-based optimization (SUNO) [link]Paper   link   bibtex  
FlowDAS: A stochastic interpolant-based framework for data assimilation. Chen, S.; Jia, Y.; Qu, Q.; Sun, H.; and Fessler, J. A. 2025.
FlowDAS: A stochastic interpolant-based framework for data assimilation [link]Paper   link   bibtex  
  2024 (6)
Using randomized \Nystrom preconditioners to accelerate variational image reconstruction. Hong, T.; Xu, Z.; Hu, J.; and Fessler, J. A. 2024.
Using randomized \Nystrom preconditioners to accelerate variational image reconstruction [link]Paper   link   bibtex  
Patch-based diffusion models beat whole-image models for mismatched distribution inverse problems. Hu, J.; Song, B.; Fessler, J. A.; and Shen, L. 2024.
Patch-based diffusion models beat whole-image models for mismatched distribution inverse problems [link]Paper   link   bibtex  
Swap-Net: A memory-efficient 2.5D network for sparse-view 3D cone beam CT reconstruction. Xu, X.; Klasky, M.; McCann, M. T.; Hu, J.; and Fessler, J. A. 2024.
Swap-Net: A memory-efficient 2.5D network for sparse-view 3D cone beam CT reconstruction [link]Paper   link   bibtex  
DiffusionBlend: learning 3D image prior through position-aware diffusion score blending for 3D computed tomography reconstruction. Song, B.; Hu, J.; Luo, Z.; Fessler, J. A.; and Shen, L. 2024.
DiffusionBlend: learning 3D image prior through position-aware diffusion score blending for 3D computed tomography reconstruction [link]Paper   link   bibtex  
Learning image priors through patch-based diffusion models for solving inverse problems. Hu, J.; Song, B.; Xu, X.; Shen, L.; and Fessler, J. A. 2024.
Learning image priors through patch-based diffusion models for solving inverse problems [link]Paper   link   bibtex  
Provable preconditioned plug-and-play approach for compressed sensing MRI reconstruction. Hong, T.; Xu, X.; Hu, J.; and Fessler, J. A. 2024.
Provable preconditioned plug-and-play approach for compressed sensing MRI reconstruction [link]Paper   link   bibtex  
  2023 (10)
Dynamic subspace estimation with Grassmannian geodesics. Blocker, C.; Raja, H.; Fessler, J. A.; and Balzano, L. 2023. University of Michigan Deep Blue Technical Report
doi   link   bibtex  
Streaming probabilistic PCA for missing data with heteroscedastic noise. Gilman, K.; Hong, D.; Fessler, J. A.; and Balzano, L. 2023.
Streaming probabilistic PCA for missing data with heteroscedastic noise [link]Paper   link   bibtex  
ALPCAH: Sample-wise heteroscedastic PCA with tail singular value regularization. Cavazos, J. A. S.; Fessler, J. A.; and Balzano, L. 2023.
ALPCAH: Sample-wise heteroscedastic PCA with tail singular value regularization [link]Paper   link   bibtex  
Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography. Schwartz, J.; Di, Z. W.; Jiang, Y.; Manassa, J.; Pietryga, J.; Qian, Y.; Cho, M.; Rowell, J.; Zheng, H.; Robinson, R.; Gu, J.; Rozeveld, S.; Ercius, P.; Fessler, J. A.; Xu, T.; Scott, M. C.; and Hovden, R. 2023.
Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography [link]Paper   link   bibtex  
Poisson-Gaussian holographic phase retrieval with score-based image prior. Li, Z.; Hu, J.; Xu, X.; Shen, L.; and Fessler, J. A. 2023.
Poisson-Gaussian holographic phase retrieval with score-based image prior [link]Paper   link   bibtex  
Dynamic subspace estimation with Grassmannian geodesics. Blocker, C. J.; Raja, H.; Fessler, J. A.; and Balzano, L. 2023.
Dynamic subspace estimation with Grassmannian geodesics [link]Paper   link   bibtex  
A complex quasi-Newton proximal method for image reconstruction in compressed sensing MRI. Hong, T.; Hernandez, L.; and Fessler, J. A. 2023.
A complex quasi-Newton proximal method for image reconstruction in compressed sensing MRI [link]Paper   link   bibtex  
Adaptive sampling for linear sensing systems via Langevin dynamics. Wang, G.; Noll, D. C.; and Fessler, J. A. 2023.
Adaptive sampling for linear sensing systems via Langevin dynamics [link]Paper   link   bibtex  
HeMPPCAT: mixtures of probabilistic principal component analysers for data with heteroscedastic noise. Xu, A. S.; Balzano, L.; and Fessler, J. A. 2023.
HeMPPCAT: mixtures of probabilistic principal component analysers for data with heteroscedastic noise [link]Paper   link   bibtex  
Training end-to-end unrolled iterative neural networks for SPECT image reconstruction. Li, Z.; Dewaraja, Y. K.; and Fessler, J. A. 2023.
Training end-to-end unrolled iterative neural networks for SPECT image reconstruction [link]Paper   link   bibtex  
  2022 (3)
Stochastic optimization of 3D non-Cartesian sampling trajectory (SNOPY). Wang, G.; Nielsen, J.; Fessler, J. A.; and Noll, D. C. 2022.
Stochastic optimization of 3D non-Cartesian sampling trajectory (SNOPY) [link]Paper   link   bibtex  
Imaging atomic-scale chemistry from fused multi-modal electron microscopy. Schwartz, J.; Di, Z. W.; Jiang, Y.; Fielitz, A. J.; Ha, D.; Perera, S. D.; Baggari, I. E.; Robinson, R. D.; Fessler, J. A.; Ophus, C.; Rozeveld, S.; and Hovden, R. 2022.
Imaging atomic-scale chemistry from fused multi-modal electron microscopy [link]Paper   link   bibtex  
Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data. Lahiri, A.; Klasky, M. L.; Fessler, J. A.; and Ravishankar, S. 2022.
Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data [link]Paper   link   bibtex  
  2021 (7)
Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT). Wang, G.; and Fessler, J. A. 2021.
Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT) [link]Paper   link   bibtex  
Bilevel methods for image reconstruction. Crockett, C.; and Fessler, J. A. 2021.
Bilevel methods for image reconstruction [link]Paper   link   bibtex  
Manifold model for high-resolution fMRI joint reconstruction and dynamic quantification. Guo, S.; Fessler, J. A.; and Noll, D. C. 2021.
Manifold model for high-resolution fMRI joint reconstruction and dynamic quantification [link]Paper   link   bibtex  
Blind primed supervised (BLIPS) learning for MR image reconstruction. Lahiri, A.; Wang, G.; Ravishankar, S.; and Fessler, J. A. 2021.
Blind primed supervised (BLIPS) learning for MR image reconstruction [link]Paper   link   bibtex  
Algorithms for Poisson phase retrieval. Li, Z.; Lange, K.; and Fessler, J. A. 2021.
Algorithms for Poisson phase retrieval [link]Paper   link   bibtex  
B-spline parameterized joint optimization of reconstruction and k-space trajectories (BJORK) for accelerated 2D MRI. Wang, G.; Luo, T.; Nielsen, J.; Noll, D. C.; and Fessler, J. A. 2021.
B-spline parameterized joint optimization of reconstruction and k-space trajectories (BJORK) for accelerated 2D MRI [link]Paper   link   bibtex  
HePPCAT: probabilistic PCA for data with heteroscedastic noise. Hong, D.; Gilman, K.; Balzano, L.; and Fessler, J. A. 2021.
HePPCAT: probabilistic PCA for data with heteroscedastic noise [link]Paper   link   bibtex  
  2020 (2)
Joint design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI. Luo, T.; Noll, D. C.; Fessler, J. A.; and Nielsen, J. 2020.
Joint design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI [link]Paper   link   bibtex  
Efficient regularized field map estimation in 3D parallel MRI. Lin, C. Y.; and Fessler, J. A. 2020.
Efficient regularized field map estimation in 3D parallel MRI [link]Paper   link   bibtex  
  2019 (9)
BCD-net for low-dose CT reconstruction: Acceleration, convergence, and generalization. Zheng, X.; Chun, I. Y.; Long, Y.; and Fessler, J. A. 2019.
BCD-net for low-dose CT reconstruction: Acceleration, convergence, and generalization [link]Paper   link   bibtex  
Momentum-Net: Fast and convergent iterative neural network for inverse problems. Chun, I. Y.; Huang, Z.; Lim, H.; and Fessler, J. A. 2019.
Momentum-Net: Fast and convergent iterative neural network for inverse problems [link]Paper   link   bibtex  
Improved low-count quantitative PET reconstruction with a variational neural network. Lim, H.; Chun, I. Y.; Dewaraja, Y. K.; and Fessler, J. A. 2019.
Improved low-count quantitative PET reconstruction with a variational neural network [link]Paper   link   bibtex  
Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression. Lahiri, A.; Fessler, J. A.; and Hernandez-Garcia, L. 2019.
Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression [link]Paper   link   bibtex  
Image reconstruction: from sparsity to data-adaptive methods and machine learning. Ravishankar, S.; Ye, J. C.; and Fessler, J. A. 2019.
Image reconstruction: from sparsity to data-adaptive methods and machine learning [link]Paper   link   bibtex  
A memory-efficient algorithm for large-scale sparsity regularized image reconstruction. Ongie, G.; Murthy, N.; Balzano, L.; and Fessler, J. A. 2019.
A memory-efficient algorithm for large-scale sparsity regularized image reconstruction [link]Paper   link   bibtex  
Optimization methods for MR image reconstruction. Fessler, J. A. 2019.
Optimization methods for MR image reconstruction [link]Paper   link   bibtex  
Convolutional analysis operator learning: Dependence on training data. Chun, I. Y.; Hong, D.; Adcock, B.; and Fessler, J. A. 2019.
Convolutional analysis operator learning: Dependence on training data [link]Paper   link   bibtex  
DECT-MULTRA: dual-energy CT image decomposition with learned mixed material models and efficient clustering. Li, Z.; Ravishankar, S.; Long, Y.; and Fessler, J. A. 2019.
DECT-MULTRA: dual-energy CT image decomposition with learned mixed material models and efficient clustering [link]Paper   link   bibtex  
  2018 (9)
A practical light transport system model for chemiluminescence distribution reconstruction. McGaffin, M. G.; Chen, H.; Fessler, J. A.; and Sick, V. 2018.
A practical light transport system model for chemiluminescence distribution reconstruction [link]Paper   link   bibtex  
Optimally weighted PCA for high-dimensional heteroscedastic data. Hong, D.; Yang, F.; Fessler, J. A.; and Balzano, L. 2018.
Optimally weighted PCA for high-dimensional heteroscedastic data [link]Paper   link   bibtex  
Fast, precise myelin water quantification using DESS MRI and kernel learning. Nataraj, G.; Nielsen, J.; Gao, M.; and Fessler, J. A. 2018. Submitted.
Fast, precise myelin water quantification using DESS MRI and kernel learning [link]Paper   link   bibtex  
Online adaptive image reconstruction (OnAIR) using dictionary models. Moore, B. E.; Ravishankar, S.; Nadakuditi, R. R.; and Fessler, J. A. 2018.
Online adaptive image reconstruction (OnAIR) using dictionary models [link]Paper   link   bibtex  
SPULTRA: low-dose CT image reconstruction with joint statistical and learned image models. Ye, S.; Ravishankar, S.; Long, Y.; and Fessler, J. A. 2018.
SPULTRA: low-dose CT image reconstruction with joint statistical and learned image models [link]Paper   link   bibtex  
Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions. Kim, D.; and Fessler, J. A. 2018.
Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions [link]Paper   link   bibtex  
Deep BCD-net using identical encoding-decoding CNN structures for iterative image recovery. Chun, I. Y.; and Fessler, J. A. 2018.
Deep BCD-net using identical encoding-decoding CNN structures for iterative image recovery [link]Paper   link   bibtex  
Convolutional analysis operator learning: acceleration and convergence. Chun, I. Y.; and Fessler, J. A. 2018.
Convolutional analysis operator learning: acceleration and convergence [link]Paper   link   bibtex  
Statistical image reconstruction using mixed Poisson-Gaussian noise model for X-ray CT. Ding, Q.; Long, Y.; Zhang, X.; and Fessler, J. A. 2018.
Statistical image reconstruction using mixed Poisson-Gaussian noise model for X-ray CT [link]Paper   link   bibtex  
  2017 (9)
Sparse-view X-ray CT reconstruction using $\ell_1$ prior with learned transform. Zheng, X.; Chun, I. Y.; Li, Z.; Long, Y.; and Fessler, J. A. 2017.
Sparse-view X-ray CT reconstruction using $\ell_1$ prior with learned transform [link]Paper   link   bibtex  
Dictionary-free MRI PERK: Parameter estimation via regression with kernels. Nataraj, G.; Nielsen, J.; Scott, C.; and Fessler, J. A. 2017.
Dictionary-free MRI PERK: Parameter estimation via regression with kernels [link]Paper   link   bibtex  
Medical image reconstruction: a brief overview of past milestones and future directions. Fessler, J. A. 2017.
Medical image reconstruction: a brief overview of past milestones and future directions [link]Paper   link   bibtex  
Low dose CT image reconstruction with learned sparsifying transform. Zheng, X.; Lu, Z.; Ravishankar, S.; Long, Y.; and Fessler, J. A. 2017.
Low dose CT image reconstruction with learned sparsifying transform [link]Paper   link   bibtex  
Convolutional dictionary learning: acceleration and convergence. Chun, I. Y.; and Fessler, J. A. 2017.
Convolutional dictionary learning: acceleration and convergence [link]Paper   link   bibtex  
PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction. Zheng, X.; Ravishankar, S.; Long, Y.; and Fessler, J. A. 2017.
PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction [link]Paper   link   bibtex  
Asymptotic performance of PCA for high-dimensional heteroscedastic data. Hong, D.; Balzano, L.; and Fessler, J. A. 2017.
Asymptotic performance of PCA for high-dimensional heteroscedastic data [link]Paper   link   bibtex  
Adaptive restart of the optimized gradient method for convex optimization. Kim, D.; and Fessler, J. A. 2017.
Adaptive restart of the optimized gradient method for convex optimization [link]Paper   link   bibtex  
Efficient sum of outer products dictionary learning (SOUP-DIL) and its application to inverse problems. Ravishankar, S.; Nadakuditi, R. R.; and Fessler, J. A. 2017.
Efficient sum of outer products dictionary learning (SOUP-DIL) and its application to inverse problems [link]Paper   link   bibtex  
  2016 (6)
Michigan image reconstruction toolbox (MIRT) for Matlab. Fessler, J. A. 2016. Available from \myurl.
link   bibtex  
Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging. Ravishankar, S.; Moore, B. E.; Nadakuditi, R. R.; and Fessler, J. A. 2016.
Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging [link]Paper   link   bibtex  
Towards a theoretical analysis of PCA for heteroscedastic data. Hong, D.; Balzano, L.; and Fessler, J. A. 2016.
Towards a theoretical analysis of PCA for heteroscedastic data [link]Paper   link   bibtex  
Fast dual proximal gradient algorithms with rate $O(1/k^{1.5})$ for convex minimization. Kim, D.; and Fessler, J. A. 2016.
Fast dual proximal gradient algorithms with rate $O(1/k^{1.5})$ for convex minimization [link]Paper   link   bibtex  
Another look at the fast iterative shrinkage/thresholding algorithm (FISTA). Kim, D.; and Fessler, J. A. 2016.
Another look at the fast iterative shrinkage/thresholding algorithm (FISTA) [link]Paper   link   bibtex  
Generalizing the optimized gradient method for smooth convex minimization. Kim, D.; and Fessler, J. A. 2016.
Generalizing the optimized gradient method for smooth convex minimization [link]Paper   link   bibtex  
  2015 (5)
Optimized first-order methods for smooth convex minimization - Supplementary material. Kim, D.; and Fessler, J. A. 2015.
link   bibtex  
Relaxed linearized algorithms for faster X-ray CT image reconstruction. Nien, H.; and Fessler, J. A. 2015.
Relaxed linearized algorithms for faster X-ray CT image reconstruction [link]Paper   link   bibtex  
Efficient sum of outer products dictionary learning (SOUP-DIL) - The $\ell_0$ method. Ravishankar, S.; Nadakuditi, R. R.; and Fessler, J. A. 2015.
Efficient sum of outer products dictionary learning (SOUP-DIL) - The $\ell_0$ method [link]Paper   link   bibtex  
On the convergence analysis of the optimized gradient methods. Kim, D.; and Fessler, J. A. 2015.
On the convergence analysis of the optimized gradient methods [link]Paper   link   bibtex  
Algorithmic design of majorizers for large-scale inverse problems. McGaffin, M. G.; and Fessler, J. A. 2015.
Algorithmic design of majorizers for large-scale inverse problems [link]Paper   link   bibtex  
  2014 (4)
Undersampled phase retrieval with outliers. Weller, D. S.; Pnueli, A.; Divon, G.; Radzyner, O.; Eldar, Y. C.; and Fessler, J. A. 2014.
Undersampled phase retrieval with outliers [link]Paper   link   bibtex  
Optimized first-order methods for smooth convex minimization. Kim, D.; and Fessler, J. A. 2014.
Optimized first-order methods for smooth convex minimization [link]Paper   link   bibtex  
Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets. Nien, H.; and Fessler, J. A. 2014.
Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets [link]Paper   link   bibtex  
A convergence proof of the split Bregman method for regularized least-squares problems. Nien, H.; and Fessler, J. A. 2014.
A convergence proof of the split Bregman method for regularized least-squares problems [link]Paper   link   bibtex  
  2012 (1)
System modeling for gamma-ray imaging systems. Lingenfelter, D. J.; and Fessler, J. A. Technical Report 411, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, March 2012.
System modeling for gamma-ray imaging systems [pdf]Paper   link   bibtex  
  2008 (1)
Fundamental performance analysis in image registration problems: \Cramer-Rao bound and its variations. Ruan, D.; and Fessler, J. A. Technical Report 386, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, March 2008.
Fundamental performance analysis in image registration problems: \Cramer-Rao bound and its variations [pdf]Paper   link   bibtex  
  2007 (1)
Adaptive ellipse tracking and a convergence proof. Ruan, D.; and Fessler, J. A. Technical Report 382, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, May 2007.
Adaptive ellipse tracking and a convergence proof [pdf]Paper   link   bibtex  
  2004 (2)
Properties of MM algorithms on convex feasible sets: extended version. Jacobson, M. W.; and Fessler, J. A. Technical Report 353, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, November 2004.
Properties of MM algorithms on convex feasible sets: extended version [pdf]Paper   link   bibtex  
Matlab tomography toolbox. Fessler, J. A. 2004. Available from \myurl.
link   bibtex  
  2003 (2)
Standard errors of mean, variance, and standard deviation estimators. Ahn, S.; and Fessler, J. A. Technical Report 413, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, July 2003.
Standard errors of mean, variance, and standard deviation estimators [pdf]Paper   link   bibtex  
Fourier-based forward and back-projectors for iterative image reconstruction. Matej, S.; Fessler, J. A.; and Kazantsev, I. G. Technical Report MIPG303, MIPG Technical Report, University of Pennsylvania, May 2003.
Fourier-based forward and back-projectors for iterative image reconstruction [pdf]Paper   link   bibtex  
  2001 (2)
Some tips for LaTeX, Matlab, and ANSI C. Fessler, J. A. Technical Report Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, November 2001. Technical report available from \myurl.
Some tips for LaTeX, Matlab, and ANSI C [pdf]Paper   link   bibtex  
Iterative tomographic image reconstruction using nonuniform fast Fourier transforms. Fessler, J. A. Technical Report Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, December 2001. Technical report available from \myurl.
Iterative tomographic image reconstruction using nonuniform fast Fourier transforms [pdf]Paper   link   bibtex  
  1998 (2)
On transformations of random vectors. Fessler, J. A. Technical Report 314, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, August 1998. ønweb
On transformations of random vectors [pdf]Paper   link   bibtex  
Computing parametric images from dynamic sequences using a QR decomposition method. Fessler, J. A. Technical Report 321, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, December 1998. ønweb
Computing parametric images from dynamic sequences using a QR decomposition method [pdf]Paper   link   bibtex  
  1997 (3)
Conjugate-gradient preconditioning methods: numerical results. Fessler, J. A. Technical Report 303, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, January 1997. ønweb
Conjugate-gradient preconditioning methods: numerical results [pdf]Paper   link   bibtex  
Spatial resolution properties of penalized weighted least-squares image reconstruction with model mismatch. Fessler, J. A. Technical Report 308, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, March 1997. ønweb
Spatial resolution properties of penalized weighted least-squares image reconstruction with model mismatch [pdf]Paper   link   bibtex  
Users guide for ASPIRE 3D image reconstruction software. Fessler, J. A. Technical Report 310, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, July 1997. ønweb
Users guide for ASPIRE 3D image reconstruction software [pdf]Paper   link   bibtex  
  1996 (2)
Signal processing pitfalls in positron emission tomography. Fessler, J. A.; and Ollinger, J. M. Technical Report 302, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, September 1996. ønweb
Signal processing pitfalls in positron emission tomography [pdf]Paper   link   bibtex  
Recursive algorithms for computing the Cramer-Rao bound. Hero, A. O.; Usman, M.; Sauve, A.; and Fessler, J. A. Technical Report 305, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, November 1996.
Recursive algorithms for computing the Cramer-Rao bound [pdf]Paper   link   bibtex  
  1995 (2)
ASPIRE 3.0 user's guide: A sparse iterative reconstruction library. Fessler, J. A. Technical Report 293, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, July 1995. ønweb
ASPIRE 3.0 user's guide: A sparse iterative reconstruction library [pdf]Paper   link   bibtex  
Resolution properties of regularized image reconstruction methods. Fessler, J. A. Technical Report 297, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, August 1995.
Resolution properties of regularized image reconstruction methods [pdf]Paper   link   bibtex  
  1994 (2)
Space-alternating generalized EM algorithms for penalized maximum-likelihood image reconstruction. Fessler, J. A.; and Hero, A. O. Technical Report 286, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, February 1994.
Space-alternating generalized EM algorithms for penalized maximum-likelihood image reconstruction [pdf]Paper   link   bibtex  
EM and gradient algorithms for transmission tomography with background contamination. Fessler, J. A. Technical Report UM-PET-JF-94-1, Cyclotron PET Facility, Univ. of Michigan, Ann Arbor, MI, 48109-2122, December 1994. ønweb
EM and gradient algorithms for transmission tomography with background contamination [pdf]Paper   link   bibtex  
  1993 (1)
Asymptotic convergence properties of EM-type algorithms. Hero, A. O.; and Fessler, J. A. Technical Report 282, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, April 1993.
Asymptotic convergence properties of EM-type algorithms [pdf]Paper   link   bibtex  
  1990 (1)
Object-based 3-D reconstruction of arterial trees from a few projections. Fessler, J. A. Ph.D. Thesis, Stanford Univ., Stanford, CA., August 1990.
Object-based 3-D reconstruction of arterial trees from a few projections [pdf]Paper   link   bibtex