Technical Reports, Etc.

  1. A O Hero, J A Fessler.
    Asymptotic convergence properties of EM-type algorithms.
    Technical Report 282, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Apr. 1993.
  2. J A Fessler, A O Hero.
    Space-alternating generalized EM algorithms for penalized maximum-likelihood image reconstruction.
    Technical Report 286, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Feb. 1994.
  3. J A Fessler.
    EM and gradient algorithms for transmission tomography with background contamination.
    Technical Report UM-PET-JF-94-1, Cyclotron PET Facility, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Dec. 1994.
  4. J A Fessler.
    ASPIRE 3.0 user's guide: A sparse iterative reconstruction library.
    Technical Report 293, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Jul. 1995.
  5. J A Fessler.
    Resolution properties of regularized image reconstruction methods.
    Technical Report 297, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Aug. 1995.
  6. J A Fessler, J M Ollinger.
    Signal processing pitfalls in positron emission tomography.
    Technical Report 302, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Sep. 1996.
  7. A O Hero, M Usman, A Sauve, J A Fessler.
    Recursive algorithms for computing the Cramer-Rao bound.
    Technical Report 305, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Nov. 1996.
  8. J A Fessler.
    Conjugate-gradient preconditioning methods: numerical results.
    Technical Report 303, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Jan. 1997.
  9. J A Fessler.
    Spatial resolution properties of penalized weighted least-squares image reconstruction with model mismatch.
    Technical Report 308, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Mar. 1997.
  10. J A Fessler.
    Users guide for ASPIRE 3D image reconstruction software.
    Technical Report 310, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Jul. 1997.
  11. J A Fessler.
    On transformations of random vectors.
    Technical Report 314, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Aug. 1998.
  12. J A Fessler.
    Computing parametric images from dynamic sequences using a QR decomposition method.
    Technical Report 321, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Dec. 1998.
  13. J A Fessler.
    Some tips for LaTeX, Matlab, and ANSI C.
    Technical Report ?, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Nov. 2001.
    Script mentioned in report.
  14. J A Fessler.
    Iterative tomographic image reconstruction using nonuniform fast Fourier transforms.
    Technical Report ?, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Dec. 2001.
  15. S Ahn, J A Fessler.
    Standard errors of mean, variance, and standard deviation estimators.
    Technical Report 413, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Jul. 2003.
  16. S Matej, J A Fessler, I G Kazantsev.
    Fourier-based forward and back-projectors for iterative image reconstruction.
    Technical Report MIPG303, MIPG Technical Report, University of Pennsylvania, May. 2003.
  17. M W Jacobson, J A Fessler.
    Properties of MM algorithms on convex feasible sets: extended version.
    Technical Report 353, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Nov. 2004.
  18. Dan Ruan, J A Fessler.
    Adaptive ellipse tracking and a convergence proof.
    Technical Report 382, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, May. 2007.
  19. Dan Ruan, J A Fessler.
    Fundamental performance analysis in image registration problems: \Cramer-Rao bound and its variations.
    Technical Report 386, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Mar. 2008.
  20. Daniel J Lingenfelter, J A Fessler.
    System modeling for gamma-ray imaging systems.
    Technical Report 411, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Mar. 2012.

Dissertation

  1. J A Fessler.
    Object-based 3-D reconstruction of arterial trees from a few projections. Stanford Univ., 1990

Software

  1. J A Fessler.
    Michigan image reconstruction toolbox (MIRT) for Matlab.
    Available from \myurl., 2016
  2. J A Fessler.
    Matlab tomography toolbox.
    Available from \myurl., 2004
  3. Cameron Blocker, Haroon Raja, J A Fessler, Laura Balzano.
    Dynamic subspace estimation with Grassmannian geodesics.
    University of Michigan Deep Blue Technical Report, 2023
  4. Alfredo Iusem, Sergio Furuie, Elias S Helou, Eduardo X Miqueles, J A Fessler, Marcelo V W Zibetti, Antonio \Leitao, Ana Gabriela Martinez, Russell Luke, Thomas Katsekpor, Jose Mario Martinez.
    South-American adventures from kayak to inverse problems: A tribute to Alvaro Rodolfo De Pierro. 2025

ARXIV papers

  1. Yixuan Jia, Siyi Chen, Yida Pan, Xiao Li, Lianghe Shi, Chanyong Jung, Haijie Yuan, Ismail Alkhouri, Yue Cynthia Wu, Saiprasad Ravishankar, J A Fessler, Qing Qu.
    ForcingDAS: unified and robust data assimilation via diffusion forcing. 2026
  2. Xiang Li, Yixuan Jia, Xiao Li, J A Fessler, Rongrong Wang, Qing Qu.
    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. 2026
  3. Hongze Yu, Yun Jiang, J A Fessler.
    Comparing implicit neural representations and B-splines for continuous function fitting from sparse samples. 2026
  4. Siddhant Gautam, Angqi Li, Prachi P Agarwal, Anil K Attili, J A Fessler, Nicole Seiberlich, Saiprasad Ravishankar.
    Scan-adaptive dynamic MRI undersampling using a dictionary of efficiently learned patterns. 2026
  5. Taewon Yang, Jason Hu, J A Fessler, Liyue Shen.
    Local patches meet global context: Scalable 3D diffusion priors for CT reconstruction. 2025
  6. Tao Hong, Umberto Villa, J A Fessler.
    A convergent generalized Krylov subspace method for compressed sensing MRI reconstruction with gradient-driven denoisers. 2025
  7. Rodrigo A Lobos, Xiaokai Wang, Rex T L Fung, Yongli He, David Frey, Dinank Gupta, Zhongming Liu, J A Fessler, Douglas C Noll.
    Spatiotemporal maps for dynamic MRI reconstruction. 2025
  8. Javier Antonio Salazar Cavazos, J A Fessler, Laura Balzano.
    ALPCAHUS: subspace clustering for heteroscedastic data. 2025
  9. Rodrigo A Lobos, Javier Salazar Cavazos, Raj Rao Nadakuditi, J A Fessler.
    Smooth optimization algorithms for global and locally low-rank regularizers. 2025
  10. Tao Hong, Zhaoyi Xu, Se Young Chun, Luis Hernandez-Garcia, J A Fessler.
    Convergent complex quasi-Newton proximal method for gradient-driven denoisers in compressed sensing MRI reconstruction. 2025
  11. Hongze Yu, J A Fessler, Yun Jiang.
    Bilevel optimized implicit neural representation for scan-specific accelerated MRI reconstruction. 2025
  12. Siddhant Gautam, Angqi Li, Nicole Seiberlich, J A Fessler, Saiprasad Ravishankar.
    Scan-adaptive MRI undersampling using neighbor-based optimization (SUNO). 2025
  13. Siyi Chen, Yixuan Jia, Qing Qu, He Sun, J A Fessler.
    FlowDAS: A stochastic interpolant-based framework for data assimilation. 2025
  14. Tao Hong, Zhaoyi Xu, Jason Hu, J A Fessler.
    Using randomized \Nystrom preconditioners to accelerate variational image reconstruction. 2024
  15. Jason Hu, Bowen Song, J A Fessler, Liyue Shen.
    Patch-based diffusion models beat whole-image models for mismatched distribution inverse problems. 2024
  16. Xiaojian Xu, Marc Klasky, Michael T McCann, Jason Hu, J A Fessler.
    Swap-Net: A memory-efficient 2.5D network for sparse-view 3D cone beam CT reconstruction. 2024
  17. Bowen Song, Jason Hu, Zhaoxu Luo, J A Fessler, Liyue Shen.
    DiffusionBlend: learning 3D image prior through position-aware diffusion score blending for 3D computed tomography reconstruction. 2024
  18. Jason Hu, Bowen Song, Xiaojian Xu, Liyue Shen, J A Fessler.
    Learning image priors through patch-based diffusion models for solving inverse problems. 2024
  19. Tao Hong, Xiaojian Xu, Jason Hu, J A Fessler.
    Provable preconditioned plug-and-play approach for compressed sensing MRI reconstruction. 2024
  20. Kyle Gilman, David Hong, J A Fessler, Laura Balzano.
    Streaming probabilistic PCA for missing data with heteroscedastic noise. 2023
  21. Javier Antonio Salazar Cavazos, J A Fessler, Laura Balzano.
    ALPCAH: Sample-wise heteroscedastic PCA with tail singular value regularization. 2023
  22. Jonathan Schwartz, Zichao Wendy Di, Yi Jiang, Jason Manassa, Jacob Pietryga, Yi-wen Qian, Mingee Cho, Jonathan Rowell, Huihuo Zheng, Richard Robinson, Junsi Gu, Steve Rozeveld, Peter Ercius, J A Fessler, Ting Xu, Mary C Scott, Robert Hovden.
    Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography. 2023
  23. Zongyu Li, Jason Hu, Xiaojian Xu, Liyue Shen, J A Fessler.
    Poisson-Gaussian holographic phase retrieval with score-based image prior. 2023
  24. Cameron J Blocker, Haroon Raja, J A Fessler, Laura Balzano.
    Dynamic subspace estimation with Grassmannian geodesics. 2023
  25. Tao Hong, Luis Hernandez, J A Fessler.
    A complex quasi-Newton proximal method for image reconstruction in compressed sensing MRI. 2023
  26. Guanhua Wang, Douglas C Noll, J A Fessler.
    Adaptive sampling for linear sensing systems via Langevin dynamics. 2023
  27. Alec S Xu, Laura Balzano, J A Fessler.
    HeMPPCAT: mixtures of probabilistic principal component analysers for data with heteroscedastic noise. 2023
  28. Zongyu Li, Yuni K Dewaraja, J A Fessler.
    Training end-to-end unrolled iterative neural networks for SPECT image reconstruction. 2023
  29. Guanhua Wang, Jon-Fredrik Nielsen, J A Fessler, Douglas C Noll.
    Stochastic optimization of 3D non-Cartesian sampling trajectory (SNOPY). 2022
  30. Jonathan Schwartz, Zichao Wendy Di, Yi Jiang, Alyssa J Fielitz, Don-Hyung Ha, Sanjaya D Perera, Ismail El Baggari, Richard D Robinson, J A Fessler, Colin Ophus, Steve Rozeveld, Robert Hovden.
    Imaging atomic-scale chemistry from fused multi-modal electron microscopy. 2022
  31. Anish Lahiri, Marc L Klasky, J A Fessler, Saiprasad Ravishankar.
    Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data. 2022
  32. Guanhua Wang, J A Fessler.
    Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT). 2021
  33. Caroline Crockett, J A Fessler.
    Bilevel methods for image reconstruction. 2021
  34. Shouchang Guo, J A Fessler, Douglas C Noll.
    Manifold model for high-resolution fMRI joint reconstruction and dynamic quantification. 2021
  35. Anish Lahiri, Guanhua Wang, Saiprasad Ravishankar, J A Fessler.
    Blind primed supervised (BLIPS) learning for MR image reconstruction. 2021
  36. Zongyu Li, Kenneth Lange, J A Fessler.
    Algorithms for Poisson phase retrieval. 2021
  37. Guanhua Wang, Tianrui Luo, Jon-Fredrik Nielsen, Douglas C Noll, J A Fessler.
    B-spline parameterized joint optimization of reconstruction and k-space trajectories (BJORK) for accelerated 2D MRI. 2021
  38. David Hong, Kyle Gilman, Laura Balzano, J A Fessler.
    HePPCAT: probabilistic PCA for data with heteroscedastic noise. 2021
  39. Tianrui Luo, Douglas C Noll, J A Fessler, Jon-Fredrik Nielsen.
    Joint design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI. 2020
  40. Claire Yilin Lin, J A Fessler.
    Efficient regularized field map estimation in 3D parallel MRI. 2020
  41. Xuehang Zheng, Il Yong Chun, Yong Long, J A Fessler.
    BCD-net for low-dose CT reconstruction: Acceleration, convergence, and generalization. 2019
  42. Il Yong Chun, Zhengyu Huang, Hongki Lim, J A Fessler.
    Momentum-Net: Fast and convergent iterative neural network for inverse problems. 2019
  43. Hongki Lim, Il Yong Chun, Yuni K Dewaraja, J A Fessler.
    Improved low-count quantitative PET reconstruction with a variational neural network. 2019
  44. Anish Lahiri, J A Fessler, Luis Hernandez-Garcia.
    Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression. 2019
  45. Sai Ravishankar, Jong Chul Ye, J A Fessler.
    Image reconstruction: from sparsity to data-adaptive methods and machine learning. 2019
  46. Greg Ongie, Naveen Murthy, Laura Balzano, J A Fessler.
    A memory-efficient algorithm for large-scale sparsity regularized image reconstruction. 2019
  47. J A Fessler.
    Optimization methods for MR image reconstruction. 2019
  48. Il Yong Chun, David Hong, Ben Adcock, J A Fessler.
    Convolutional analysis operator learning: Dependence on training data. 2019
  49. Zhipeng Li, Saiprasad Ravishankar, Yong Long, J A Fessler.
    DECT-MULTRA: dual-energy CT image decomposition with learned mixed material models and efficient clustering. 2019
  50. Madison G McGaffin, Hao Chen, J A Fessler, Volker Sick.
    A practical light transport system model for chemiluminescence distribution reconstruction. 2018
  51. David Hong, Fan Yang, J A Fessler, Laura Balzano.
    Optimally weighted PCA for high-dimensional heteroscedastic data. 2018
  52. Gopal Nataraj, Jon-Fredrik Nielsen, Mingjie Gao, J A Fessler.
    Fast, precise myelin water quantification using DESS MRI and kernel learning.
    Submitted., 2018
  53. Brian E Moore, Saiprasad Ravishankar, Raj Rao Nadakuditi, J A Fessler.
    Online adaptive image reconstruction (OnAIR) using dictionary models. 2018
  54. Siqi Ye, Saiprasad Ravishankar, Yong Long, J A Fessler.
    SPULTRA: low-dose CT image reconstruction with joint statistical and learned image models. 2018
  55. Donghwan Kim, J A Fessler.
    Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions. 2018
  56. Il Yong Chun, J A Fessler.
    Deep BCD-net using identical encoding-decoding CNN structures for iterative image recovery. 2018
  57. Il Yong Chun, J A Fessler.
    Convolutional analysis operator learning: acceleration and convergence. 2018
  58. Qiaoqiao Ding, Yong Long, Xiaoqun Zhang, J A Fessler.
    Statistical image reconstruction using mixed Poisson-Gaussian noise model for X-ray CT. 2018
  59. Xuehang Zheng, Il Yong Chun, Zhipeng Li, Yong Long, J A Fessler.
    Sparse-view X-ray CT reconstruction using $\ell_1$ prior with learned transform. 2017
  60. Gopal Nataraj, Jon-Fredrik Nielsen, Clayton Scott, J A Fessler.
    Dictionary-free MRI PERK: Parameter estimation via regression with kernels. 2017
  61. J A Fessler.
    Medical image reconstruction: a brief overview of past milestones and future directions. 2017
  62. Xuehang Zheng, Zening Lu, Saiprasad Ravishankar, Yong Long, J A Fessler.
    Low dose CT image reconstruction with learned sparsifying transform. 2017
  63. Il Yong Chun, J A Fessler.
    Convolutional dictionary learning: acceleration and convergence. 2017
  64. Xuehang Zheng, Saiprasad Ravishankar, Yong Long, J A Fessler.
    PWLS-ULTRA: An efficient clustering and learning-based approach for low-dose 3D CT image reconstruction. 2017
  65. David Hong, Laura Balzano, J A Fessler.
    Asymptotic performance of PCA for high-dimensional heteroscedastic data. 2017
  66. Donghwan Kim, J A Fessler.
    Adaptive restart of the optimized gradient method for convex optimization. 2017
  67. Saiprasad Ravishankar, Brian E Moore, Raj Rao Nadakuditi, J A Fessler.
    Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging. 2016
  68. David Hong, Laura Balzano, J A Fessler.
    Towards a theoretical analysis of PCA for heteroscedastic data. 2016
  69. Donghwan Kim, J A Fessler.
    Fast dual proximal gradient algorithms with rate $O(1/k^{1.5})$ for convex minimization. 2016
  70. Donghwan Kim, J A Fessler.
    Another look at the fast iterative shrinkage/thresholding algorithm (FISTA). 2016
  71. Donghwan Kim, J A Fessler.
    Generalizing the optimized gradient method for smooth convex minimization. 2016
  72. Donghwan Kim, J A Fessler.
    Optimized first-order methods for smooth convex minimization - Supplementary material. 2015
  73. Hung Nien, J A Fessler.
    Relaxed linearized algorithms for faster X-ray CT image reconstruction. 2015
  74. Saiprasad Ravishankar, Raj Rao Nadakuditi, J A Fessler.
    Efficient sum of outer products dictionary learning (SOUP-DIL) - The $\ell_0$ method. 2015
  75. Saiprasad Ravishankar, Raj Rao Nadakuditi, J A Fessler.
    Efficient sum of outer products dictionary learning (SOUP-DIL) and its application to inverse problems. 2017
  76. Donghwan Kim, J A Fessler.
    On the convergence analysis of the optimized gradient methods. 2015
  77. Madison G McGaffin, J A Fessler.
    Algorithmic design of majorizers for large-scale inverse problems. 2015
  78. Daniel S Weller, Ayelet Pnueli, Gilad Divon, Ori Radzyner, Yonina C Eldar, J A Fessler.
    Undersampled phase retrieval with outliers. 2014
  79. Donghwan Kim, J A Fessler.
    Optimized first-order methods for smooth convex minimization. 2014
  80. Hung Nien, J A Fessler.
    Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets. 2014
  81. Hung Nien, J A Fessler.
    A convergence proof of the split Bregman method for regularized least-squares problems. 2014
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