Publications Journal Papers

N. Elata, B. Kawar, T. Michaeli and M. Elad, GSURE-Based Diffusion Model Training with Corrupted Data, submitted to Transactions on Machine Learning Research.

O. Belhasin, Y. Romano, D. Freedman, E. Rivlin, and M. Elad, Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence.

M. Elad, B. Kawar and G. Vaksman, Image Denoising: The Deep Learning Revolution and Beyond - A Survey Paper, SIAM Journal on Imaging Sciences, Volume 16, No. 3, Pages 1594-1654, September 2023.

B. Kawar, R. Ganz and M. Elad, Enhancing diffusion-based image synthesis with robust classifier guidance, Transactions on Machine Learning Research (TMLR), March 2023.

R. Ganz and M. Elad, BIGRoC: Boosting Image Generation via a Robust Classifier, Transactions on Machine Learning Research (TMLR), February 2023.

A. Aberdam, A. Golts and M. Elad, Ada-LISTA: Learned Solvers Adaptive to Varying Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, No. 12, December 2022.

H. Talebi, D. Kelly, X. Luo, I. Garcia Dorado, F. Yang, P. Milanfar and M. Elad, Better Compression with Deep Pre-Editing, IEEE Transactions on Image Processing, Vol. 30, No. 8, Pages 6673-6685, August 2021.

M. Scetbon, M. Elad, and P. Milanfar, Deep K-SVD Denoising, IEEE Transactions on Image Processing, Vol. 30, Pages 5944-5955, June 2021. software

R. Khatib, D. Simon, and M. Elad, Learned Greedy Method (LGM): A novel Neural Architecture for Sparse Coding and Beyond, Journal of Visual Communication and Image Representation, Vol. 77, March 2021.

A. Golts, D. Freedman, and M. Elad, Deep-Energy: Unsupervised Training of Deep Neural Networks, IEEE Transactions on Image Processing, Vol. 15, No. 2, Pages 324-338, February 2021. software

M. Elad, D. Simon, and A. Aberdam, Another Step Toward Demystifying Deep Neural Networks, PNAS Commentary, October 2020.

A. Golts, D. Freedman, and M. Elad, Unsupervised Single Image Dehazing Using Dark Channel Prior Loss, IEEE Transactions on Image Processing, Vol. 29, No. 1, Pages 2692-2701, January 2020. software

Y. Romano, A. Aberdam, J. Sulam, and M. Elad, Adversarial Noise Attacks of Deep Learning Architectures – Stability Analysis via Sparse Modeled Signals, Journal of Mathematical Imaging and Vision, Pages 1-15, 2019.

J. Sulam, A. Aberdam, A. Beck, and M. Elad, On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), March 2019.

A. Aberdam, J. Sulam, and M. Elad, Multi Layer Sparse Coding: the Holistic Way, SIAM Journal on Mathematics of Data Science (SIMODS), Vol. 1, No. 1, Pages 46-77, February 2019. software

J. Sulam, V. Papyan, Y. Romano, and M. Elad, Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning, IEEE Trans. on Signal Processing, Vol. 66, No. 15, Pages 4090-4104, August 2018.

V. Papyan, Y. Romano, J. Sulam, and M. Elad, Theoretical Foundations of Deep Learning via Sparse Representations, IEEE Signal Processing Magazine, Vol. 35, No. 4, Pages 72-89, June 2018.

V. Papyan, Y. Romano, and M. Elad, Convolutional Neural Networks Analyzed via Convolutional Sparse Coding, Journal of Machine Learning Research, Vol. 18, Pages 1-52, July 2017.

D. Boublil, M. Elad, J. Shtok, and M. Zibulevsky, Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks, IEEE Transactions on Medical Imaging, Vol. 34, No. 7, Pages 1474-1485, July 2015.