Journal Publications | Tomer Michaeli

E. Nehme and T. Michaeli, “Generative AI for Solving Inverse Problems in Computational Imaging,” XRDS: Crossroads, The ACM Magazine for Students, 2025.
M. Kleiner, L. Michaeli and T. Michaeli, “Coherence awareness in diffractive neural networks,” Lasers and Photonics Reviews, 2024.
D. Garibi, S. Yadin, R. Paiss, O. Tov, S. Zada, A. Ephrat, T. Michaeli, I. Mosseri and T. Dekel, “TokenVerse: Versatile multi-concept personalization in token modulation space,” ACM Transactions on Graphics, 2025 (SIGGRAPH Proc.). Best paper award.
R. Yosifof, S.Yadin, T. Michaeli, D. Fisher-Gewirtzman, G. Austern, “Convolutional neural networks for predicting the perceived density of large urban fabrics,” Computers, Environment and Urban Systems, 2025.

B. Kawar, N. Elata, T. Michaeli and M. Elad, “GSURE-Based diffusion model training with corrupted data,” Transactions on Machine Learning Research (TMLR), 2024.
H. Chefer, S. Zada, R. Paiss, A. Ephrat, O. Tov, M. Rubinstein, L. Wolf, T. Dekel, T. Michaeli and I. Mosseri, “Still-Moving: Customized video generation without customized video data,” ACM Transactions on Graphics, 2024 (SIGGRAPH Asia Proc.).

Hanan Tanasra, Tamar Rott Shaham, Tomer Michaeli, Guy Austern and Shany Barath, “Automation in Design: A Comparative study of image-to-image models for interior design generation,” Buildings, vol. 13, no. 7, 2023.
V. Khalfin Fekson, T. Michaeli, K. S. Rosch, B. L. Schlaggar and T. Horowitz-Kraus, “Characterizing different cognitive and neurobiological profiles in a community sample of children using a nonparametric approach: An fMRI study,” Developmental Cognitive Neuroscience, vol. 60, 2023.

E. Nehme, B. Ferdman, L. E. Weiss, T. Naor, D. Freedman, T. Michaeli and Y. Shechtman, “Learning optimal wavefront shaping for multi-channel imaging,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 43, no. 7, 2021. [Supplementary]

E. Nehme, D. Freedman, R. Gordon, B. Ferdman, L. E. Weiss, O. Alalouf, T. Naor, R. Orange, T. Michaeli and Y. Shechtman, “DeepSTORM3D: Dense three dimensional localization microscopy and PSF design by deep learning,” Nature Methods, vol. 17, pp. 734–740, Jun. 2020.

E. Hershko, L. E. Weiss, T. Michaeli and Y. Shechtman, “Multicolor localization microscopy by deep learning,” Optics Express, vol. 27, no. 5, pp. 6158-6183, Mar. 2019.
R. Mulayoff and T. Michaeli, “On the minimal overcompleteness allowing universal sparse representation,” IEEE Transactions on Information Theory, vol. 65 , no. 6, pp. 3585 – 3599, Jun. 2019.

E. Nehme, L. E. Weiss, T. Michaeli and Y. Shechtman, “Deep-STORM: Super resolution single molecule microscopy by deep learning,” Optica, vol. 5, no. 4, pp. 458-464, Apr. 2018.

T. Dekel, T. Michaeli, M. Irani and W. T. Freeman, “Revealing and modifying non-local variations in a single image,” ACM Transactions on Graphics (TOG), vol. 34, no. 6, Nov. 2015 (SIGGRAPH Asia Proc.). See project webpage.

D. Sigalov, T. Michaeli and Y. Oshman, “LMMSE Filtering in feedback systems with white random modes: Application to tracking in clutter,” IEEE Transactions on Automatic Control, vol.59, no. 9, pp. 2549-2554, Sep. 2014.

A. Nevet, T. Michaeli and M. Orenstein, “Second-order optical coherence tomography: Depth imaging through spatio-temporally turbulent media,” The Journal of the Optical Society of America B, vol. 30, no. 2, pp. 258-265, Feb. 2013.

T. Michaeli, Y. C. Eldar and G. Sapiro, “Semi-supervised single- and multi-domain regression with multi-domain training,” Information and Inference, vol. 1, no. 1, pp. 68-97, Dec. 2012.
Z. Ben-Haim, T. Michaeli and Y. C. Eldar, “Performance bounds and design criteria for estimating finite rate of innovation signals,”IEEE Transaction on Information Theory, vol. 58, no. 8, pp. 4993-5015, Aug. 2012.
T. Michaeli, D. Sigalov and Y. C. Eldar, “Partially linear estimation with application to sparse signal recovery from measurement pairs,”IEEE Transactions on Signal Processing, vol. 60, no. 5, pp. 2125-2137, May 2012.
T. Michaeli and Y. C. Eldar, “Xampling at the rate of innovation,” IEEE Transactions on Signal Processing, vol. 60, no. 3, pp. 1121-1133, Mar. 2012.

T. Michaeli, V. Pohl and Y. C. Eldar, “U-Invariant sampling: Extrapolation and causal interpolation from generalized samples,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 2085-2100, May 2011.
T. Michaeli and Y. C. Eldar, “Hidden relationships: Bayesian estimation with partial knowledge,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 1933-1948, May 2011. See project webpage.

A. Danon, N. Zeevi-Levin, D. Y. Pinkovich, T. Michaeli, A. Berkovich, M. Flugelman, Y. C. Eldar, M. R. Rosen and O. Binah, “Hypoxia causes connexin43 internalization in neonatal rat ventricular myocytes,” General Physiology and Biophysics, vol. 29, no. 3, pp. 222-233, 2010.
E. Matusiak, T. Michaeli and Y. C. Eldar, “Non-invertible Gabor transforms,” IEEE Transactions on Signal Processing, vol. 58, no. 5, pp. 2597-2612, May 2010.
T. Faktor, T. Michaeli and Y. C. Eldar, “Nonlinear and nonideal sampling revisited,” IEEE Signal Processing Letters, vol. 17, no. 2, pp. 205-208, Feb. 2010.

Y. C. Eldar and T. Michaeli, “Beyond bandlimited sampling,” IEEE Signal Processing Magazine, vol. 26, no. 3, pp. 48-68, May 2009.
T. Michaeli and Y. C. Eldar, “High rate interpolation of random signals from nonideal samples,” IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 977-992, Mar. 2009.