Publications Other

1.   M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, Springer, 2010. A Matlab-package that reproduces the book's figures and contains most of the discussed algorithms is available here. software

1.  E. Zisselman, A. Adler, and M. Elad, Compressed Learning for Image Classification: A Deep Neural Network Approach, in "Processing, Analyzing and Learning of Images, Shapes and Forms: Part 1", Edited by Ron Kimmel and Xu-Cheng Tai, Elsevier, North Holland, 2018.

2.  D. Batenkov, Y. Romano, and M. Elad, On the Global-Local Dichotomy in Sparsity Modeling, in "Compressed Sensing and its Applications", edited by Boche H., Caire G., Calderbank R., März M., Kutyniok G., Mathar R, Applied and Numerical Harmonic Analysis. Birkhäuser.

3.  M. Elad, Five Lectures on Sparse and Redundant Representations Modelling of Images, in "Mathematics in Image Processing", Edited by Hongkai Zhao, AMS Publishing. 2010.

4.  M. Protter and M. Elad, Super-Resolution With Probabilistic Motion Estimation, in "Super-Resolution Imaging", Edited by Peyman Milanfar, CRC 2010.

5.  S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Simultaneous Demosaicking and Resolution Enhancement From Under-Sampled Image Sequences, in "Single-Sensor Imaging: Methods and Applications for Digital Cameras", Edited by Rastislav Lukac, CRC Press, 2008.

1.  G. Ohayon, T. Michaeli and M. Elad, Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration, arXiv:2410.00418, Obtober 2024. software

2.  R. Ganz and M. Elad, Text-to-Image Generation Via Energy-Based CLIP, arXiv:2408.17046, September 2024.

3.  N. Elata, T. Michaeli and M. Elad, Zero-Shot Image Compression with Diffusion-Based Posterior Sampling, arXiv:2407.09896, July 2024.

4.  N. Elata, T. Michaeli and M. Elad, Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling, arXiv:2407.08256, July 2024.

5.  O. Belhasin, I. Kligvasser, G. Leifman, R. Cohen, E. Rainaldi, L-F. Cheng, N. Varma, P. Varghese, E. Rivlin, and M. Elad, Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models, arXiv:2405.11566, May 2024.

6.  G. Ohayon, M. Elad and T. Michaeli, Perceptual Fairness in Image Restoration, arXiv:2405.13805, May 2024.

7.  L. Ringel, R. Cohen, D. Freedman, M. Elad, and Yaniv Romano, Early Time Classification with Accumulated Accuracy Gap Control, arXiv:2402.00857, February 2024.

8.  G. Ohayon, M. Elad and T. Michaeli, The Perception-Robustness Tradeoff in Deterministic Image Restoration, arXiv:2311.09253, 14 November 2023.

9.  N. Elata, B. Kawar, T. Michaeli and M. Elad, Nested Diffusion Processes for Anytime Image Generation, arXiv:2305.19066, October 2023.

10.  R. Benita, M. Elad, and J. Keshet, DIFFAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform Generation, arXiv:2310.01381, October 2023.

11.  R. Ganz and M. Elad, CLIPAG: Towards Generator-Free Text-to-Image Generation, arXiv:2306.16805, July 2023.

12.  T. Adrai, G. Ohayon, T. Michaeli, and M. Elad, Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration, arXiv:2306.02342, June 2023. software

13.  B. Kawar, N. Elata, T. Michaeli and M. Elad, GSURE-Based Diffusion Model Training with Corrupted Data, arXiv:2305.13128, May 2023.

14.  O. Belhasin, Y. Romano, D. Freedman, E. Rivlin, and M. Elad, Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems, arXiv:2305.10124, May 2023.

15.  G. Leifman, I. Kligvasser, R. Goldenberg, M. Elad and E. Rivlin, Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time, arXiv:2305.10026, May 2023.

16.  I. Kligvasser, G. Leifman, R. Goldenberg, E. Rivlin and Michael Elad, Semi-supervised Quality Evaluation of Colonoscopy Procedures, arXiv:2305.10090, May 2023.

17.  T. Blau, R. Ganz, C. Baskin, M. Elad and A. Bronstein, Classifier Robustness Enhancement Via Test-Time Transformation, arXiv:2303.15409, March 2023.

18.  G. Bar-Shalom, G. Leifman, M. Elad and E. Rivlin, Weakly-supervised Representation Learning for Video Alignment and Analysis, arXiv:2302.04064, February 2023.

19.  M. Elad, B. Kawar and G. Vaksman, Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper, arXiv:2301.03362, January 2023.

20.  N. Torem, R. Ronen, Y.Y. Schechner and M. Elad, Complex-valued Retrievals From Noisy Images Using Diffusion Models, arXiv:2212.03235, December 2022.

21.  G. Kutiel, R. Cohen, M. Elad and D. Freedman, What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems, arXiv:2211.15211, November 2022.

22.  S. Man, G. Ohayon, T. Adrai, and M. Elad, High-Perceptual Quality JPEG Decoding via Posterior Sampling, arXiv:2211.11827, November 2022.

23.  G. Vaksman and M. Elad, Patch-Craft Self-Supervised Training for Correlated Image Denoising, arXiv:2211.09919, NOvember 2022.

24.  G. Ohayon, T. Adrai, M. Elad and T. Michaeli, Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality, arXiv:2211.08944, November 2022.

25.  B. Kawar, J. Song, S, Ermon and M. Elad, JPEG Artifact Correction using Denoising Diffusion Restoration Models, arXiv:2209.11888, September 2022.

26.  B. Kawar, R. Ganz, and M. Elad, Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance, arXiv:2208.08664, August 2022.

27.  T. Blau, R. Ganz, B. Kawar, A. Bronstein and M. Elad, Threat model-agnostic adversarial defense using diffusion models, arXiv:2207.08089, July 2022.

28.  R. Ganz, B. Kawar and M. Elad, Do Perceptually Aligned Gradients Imply Adversarial Robustness?, arXiv:2207.11378, July 2022.

29.  B. Kawar, M. Elad, S. Ermon and J. Song, Denoising Diffusion Restoration Models, arXiv:2201.11793, January 2022. software

30.  R. Ganz and M. Elad, BIGRoC: Boosting Image Generation via a Robust Classifier, arXiv:2108.03702, August 2021.

31.  B. Kawar, G. Vaksman, and M. Elad, SNIPS: Solving Noisy Inverse Problems Stochastically, arXiv:2105.14951, May 2021. software

32.  R. Ganz and M. Elad, Improved Image Generation via Sparse Modeling, arXiv:2104.00464, April 2021.

33.  G. Vaksman, M. Elad and P. Milanfar, Patch Craft: Video Denoising by Deep Modeling and Patch Matching, arXiv:2103.13767, March 2021.

34.  G. Ohayon, T. Adrai, G. Vaksman, M. Elad, and P. Milanfar, High Perceptual Quality Image Denoising with a Posterior Sampling CGAN, arXiv:2103.04192, March 2021.

35.  B. Kawar, G. Vaksman, and M. Elad, Stochastic Image Denoising by Sampling from the Posterior Distribution, arXiv:2101.09552, January 2021.

36.  R. Khatib, D. Simon, and M. Elad, Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond, arXiv:2010.07069, October 2020. software

37.  X. Luo, H. Talebi, F. Yang, M. Elad, and P. Milanfar, The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study, arXiv:2008.00605, August 2020.

38.  R. Cohen, M. Elad and P. Milanfar, Regularization by Denoising via Fixed-Point Projection (RED-PRO), arXiv:2008.00226, August 2020.

39.  A. Aberdam, D. Simon and M. Elad, When and How Can Deep Generative Models be Inverted?, arXiv:2006.15555, June 2020.

40.  H. Talebi, D. Kelly, X. Luo, I. Garcia Dorado, F. Yang, P. Milanfar, and M. Elad, Better Compression with Deep Pre-Editing, arXiv:2002.00113, February 2020.

41.  A. Aberdam, A. Golts,and M. Elad, Ada-LISTA: Learned Solvers Adaptive to Varying Models, arXiv:2001.08456, January 2020. software

42.  G. Vaksman, M. Elad and P. Milanfar, Low-Weight and Learnable Image Denoising, 1911.07167, November 2019.

43.  M. Scetbon, M. Elad, and P. Milanfar, Deep K-SVD Denoising, 1909.13164, September 2019. software

44.  D. Simon and M. Elad, Rethinking the CSC Model for Natural Images, 1909.05742, Sedptember 2019.

45.  G. Mataev, M. Elad and P. Milanfar, DeepRED: Deep Image Prior Powered by RED, 1903.10176, March 2019.

46.  Y. Romano, A. Aberdam, J. Sulam and M. Elad, Adversarial Noise Attacks of Deep Learning Architectures - Stability Analysis via Sparse Modeled Signals, 1805.11596, November 2018.

47.  E. Zisselman, J. Sulam, and M. Elad, A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model, 1811.00312, November 2018 software

48.  Alon Brifman, Yaniv Romano, and Michael Elad, Unified Single-Image and Video Super-Resolution via Denoising Algorithms, 1810.01938, October 2018.

49.  Ives Rey-Otero, Jeremias Sulam, and Michael Elad, Variations on the CSC Model, 1810.01169, October 2018.

50.  D. Simon, J. Sulam, Y. Romano, Y.M. Lu, and M. Elad, Improving Pursuit Algorithms Using Stochastic Resonance, 1806.10171, June 2018.

51.  Y. Yankelevsky and M. Elad, Finding GEMS: Multi-Scale Dictionaries for High-Dimensional Graph Signals, 1806.05356, June 2018.

52.  J. Sulam, A. Aberdam, A. Beck, and M. Elad, On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks, 1806.00701, June 2018.

53.  A. Golts, D. Friedman, and M. Elad, Deep Energy: Using Energy Functions for Unsupervised Training of DNNs, 1805.12355, May 2018.

54.  T. Hong, Y. Romano, and M. Elad, Acceleration of RED via Vector Extrapolation, 1805.02158, May 2018.

55.  A. Aberdam, J. Sulam and M. Elad, Multi Layer Sparse Coding: the Holistic Way, 1804.09788, April 2018.

56.  Y. Dar, M. Elad and A.M. Bruckstein, Compression for Multiple Reconstructions, 1802.03937, February 2018

57.  Y. Dar, M. Elad and A.M. Bruckstein, System-Aware Compression, 1801.04853, January 2018.

58.  Y. Dar, M. Elad and A.M. Bruckstein, Optimized Pre-Compensating Compression, 1711.07901, November 2017.

59.  Y. Dar, M. Elad and A.M. Bruckstein, Restoration by Compression, 1711.05147, November 2017.

60.  J. Sulam, V. Papyan, Y. Romano, and M. Elad, Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning, 1708.08705, August 2017.

61.  V. Papyan, J. Sulam, and M. Elad, Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding, 1707.06066, July 2017.

62.  Vardan Papyan, Yaniv Romano, Jereias Sulam, and Michael Elad, Convolutional Dictionary Learning via Local Processing, 1705.03239, May 2017.

63.  Dmitry Batenkov, Yaniv Romano, and Michael Elad, On the Global-Local Dichotomy in Sparsity Modeling, 1702.03446, February 2017.

64.  Yaniv Romano, Michael Elad and Peyman Milanfar, The Little Engine that Could: Regularization by Denoising (RED), 1611.02862, November 2016.

65.  Amir Adler, Michael Elad, and Michael Zibulevsky, Compressed Learning: A Deep Neural Network Approach, 1610.09615, October 2016.

66.  Yael Yankelevsky and Michael Elad, Structure-Aware Classification using Supervised Dictionary Learning, 1609.09199, September 2016.

67.  Yi Ren, Yaniv Romano, and Michael Elad, Example-Based Image Synthesis via Randomized Patch-Matching, 1609.07370, September 2016.

68.  Michael Elad and Peyman Milanfar, Style-Transfer via Texture-Synthesis, 1609.03057, September 2016.

69.  Vardan Papyan, Yaniv Romano and Michael Elad, Convolutional Neural Networks Analyzed via Convolutional Sparse Coding, 1607.08194, July 2016.

70.  Vardan Papyan, Jeremias Sulam, and Michael Elad, Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding, 1607.02009, July 2016.

71.  Vardan Papyan, Jeremias Sulam, and Michael Elad, Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding, 1607.02005, July 2016.

72.  Amir Adler, David Boublil, Michael Elad, and Michael Zibulevsky, A Deep Learning Approach to Block-based Compressed Sensing of Images, 1606.01519, June 2016.

73.  Yaniv Romano and Michael Elad, Con-Patch: When a Patch Meets its Context, 1603.06812, June 2016.

74.  Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, and Michael Elad, Trainlets: Dictionary Learning in High Dimensions, 1602.00212, May 2016.

75.  Yehuda Dar, Alfred M. Bruckstein, Michael Elad, and Raja Giryes, Postprocessing of Compressed Images via Sequential Denoising, 1510.09041, March 2016.

76.  Gregory Vaksman, Michael Zibulevsky, and Michael Elad, Patch-Ordering as a Regularization for Inverse Problems in Image Processing, 1602.08510, February 2016.

77.  Arie Rond, Raja Giryes, and Michael Elad, Poisson Inverse Problems by the Plug-and-Play scheme, 1511.02500, November 2015.

78.  Alona Goltz and Michael Elad, Linearized Kernel Dictionary Learning, 1509.05634, Spetember 2015.

79.  Wen-Ze Shao and Michael Elad, Simple, Accurate, and Robust Nonparametric Blind Super-Resolution, 1503.03187, March 2015.

80.  Yaniv Romano and Michael Elad, Boosting of Image Denoising Algorithms, 1502.06220, March 2015.

81.  Wen-Ze Shao, Hai-Bo Li, and Michael Elad, Bi-l0-l2-Norm Regularization for Blind Motion Deblurring, 1408.4712, August 2014.

82.  Raja Giryes, Michael Elad, and Alfred Bruckstein, Sparsity Based Methods for Overparametrized Variational Problems, 1405.4969, May 2014.

83.  Joseph Shtok, Michael Zibulevsky, and Michael Elad, Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks, 1311.7251, November 2013.

84.  Raja Giryes and Michael Elad, Sparsity Based Poisson Denoising with Dictionary Learning, 1309.4306, September 2013.

85.  Idan Ram, Michael Elad and Israel Cohen, Image Processing using Smooth Ordering of its Patches, 1210.3832, October 2012.

86.  Raja Giryes, Sangnam Nam, Michael Elad, RГ©mi Gribonval, and Mike E. Davies, Greedy-Like Algorithms for the Cosparse Analysis Model, 1207.2456, January 2013.

87.  Idan Ram, Michael Elad, and Israel Cohen, Can we allow linear dependencies in the dictionary in the sparse synthesis framework?, 1210.3832, October 2012.

88.  Tomer Peleg and Michael Elad, Performance Guarantees of the Thresholding Algorithm for the Co-Sparse Analysis Model, 1203.2769, March 2012.

89.  Tomer Peleg, Yonina C. Eldar, and Michael Elad, Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery, 1010.5734, March 2012.

90.  Idan Ram, Michael Elad, and Israel Cohen, Redundant Wavelets on Graphs and High Dimensional Data Clouds, 1111.4619, November 2011.

91.  Sangnam Nam, Mike E. Davies, Michael Elad, and RГ©mi Gribonval, The Cosparse Analysis Model and Algorithms, 1106.4987, June 2011.

92.  Idan Ram, Michael Elad, and Israel Cohen, Generalized Tree-Based Wavelet Transform, 1011.4615, February 2011.

93.  Raja Giryes and Michael Elad, RIP-Based Near-Oracle Performance Guarantees for Subspace-Pursuit, CoSaMP, and Iterative Hard-Thresholding, 1005.4539, May 2010.

94.  Joseph Shtok, Michael Zibulevsky, and Michael Elad, Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning, 1004.4373, April 2010.

95.  Joseph Shtok and Michael Elad, Analysis of Basis Pursuit Via Capacity Sets, 1004.4329, April 2010.

96.  Raja Giryes, Michael Elad, and Yonina C. Eldar, The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods, 1003.3985, March 2010.

97.  Javier Turek, Irad Yavneh, Matan Protter, and Michael Elad, On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary, 1003.3984, March 2010.

98.  Zvika Ben-Haim, Yonina C. Eldar, and Michael Elad, Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise, 0903.4579, March 2009.

1.  M. Elad, Mathematical Methods for Engineering, (in Hebrew), Technion 2006.

2.  M. Elad, Elementary Course in Signal and Image Processing, (in Hebrew), Technion 2005.

3.  M. Elad, Introduction to Image Processing, (in Hebrew), Lecture Notes, Technion 1999.

4.  M. Elad, Numerical Methods in Optimization, (in Hebrew), Lecture Notes, Technion 1998.