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ORCIDMichael Elad
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- affiliation: Technion - Israel Institute of Technology, Haifa, Israel
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2020 – today
- 2024
[j125]Omer Belhasin
, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad:
Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3321-3333 (2024)- [j124]Bahjat Kawar, Noam Elata, Tomer Michaeli, Michael Elad:
GSURE-Based Diffusion Model Training with Corrupted Data. Trans. Mach. Learn. Res. 2024 (2024) - [c83]Noam Elata, Tomer Michaeli, Michael Elad:
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling. ECCV (78) 2024: 290-308 - [c82]Roi Benita, Michael Elad, Joseph Keshet:
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform Generation. ICLR 2024 - [c81]Guy Ohayon, Tomer Michaeli, Michael Elad:
The Perception-Robustness Tradeoff in Deterministic Image Restoration. ICML 2024 - [c80]Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano:
Early Time Classification with Accumulated Accuracy Gap Control. ICML 2024 - [c79]Roy Ganz, Michael Elad:
CLIPAG: Towards Generator-Free Text-to-Image Generation. WACV 2024: 3831-3841 - [c78]Noam Elata, Bahjat Kawar, Tomer Michaeli, Michael Elad:
Nested Diffusion Processes for Anytime Image Generation. WACV 2024: 4995-5004 - [c77]Guy Bar-Shalom, George Leifman, Michael Elad:
Weakly-Supervised Representation Learning for Video Alignment and Analysis. WACV 2024: 6895-6904 - [i99]Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano:
Early Time Classification with Accumulated Accuracy Gap Control. CoRR abs/2402.00857 (2024) - [i98]Omer Belhasin, Idan Kligvasser, George Leifman, Regev Cohen, Erin Rainaldi, Li-Fang Cheng, Nishant Verma, Paul Varghese, Ehud Rivlin, Michael Elad:
Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models. CoRR abs/2405.11566 (2024) - [i97]Guy Ohayon, Michael Elad, Tomer Michaeli:
Perceptual Fairness in Image Restoration. CoRR abs/2405.13805 (2024) - [i96]Shelly Golan, Roy Ganz, Michael Elad:
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination. CoRR abs/2405.16260 (2024) - [i95]Noam Elata, Tomer Michaeli, Michael Elad:
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling. CoRR abs/2407.08256 (2024) - [i94]Noam Elata, Tomer Michaeli, Michael Elad:
Zero-Shot Image Compression with Diffusion-Based Posterior Sampling. CoRR abs/2407.09896 (2024) - [i93]Idan Kligvasser, Regev Cohen, George Leifman, Ehud Rivlin, Michael Elad:
Anchored Diffusion for Video Face Reenactment. CoRR abs/2407.15153 (2024) - [i92]Roy Ganz, Michael Elad:
Text-to-Image Generation Via Energy-Based CLIP. CoRR abs/2408.17046 (2024) - 2023
- [j123]Michael Elad, Bahjat Kawar, Gregory Vaksman:
Image Denoising: The Deep Learning Revolution and Beyond - A Survey Paper. SIAM J. Imaging Sci. 16(3): 1594-1654 (2023) - [j122]Roy Ganz, Michael Elad:
BIGRoC: Boosting Image Generation via a Robust Classifier. Trans. Mach. Learn. Res. 2023 (2023) - [j121]Bahjat Kawar, Roy Ganz, Michael Elad:
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. Trans. Mach. Learn. Res. 2023 (2023) - [c76]Sean Man, Guy Ohayon, Theo Adrai, Michael Elad:
High-Perceptual Quality JPEG Decoding via Posterior Sampling. CVPR Workshops 2023: 1272-1282 - [c75]Gregory Vaksman, Michael Elad:
PatchCraft Self-Supervised Training for Correlated Image Denoising. CVPR 2023: 5795-5804 - [c74]Idan Kligvasser, George Leifman, Roman Goldenberg, Ehud Rivlin, Michael Elad:
Semi-supervised Quality Evaluation of Colonoscopy Procedures. ICCV (Workshops) 2023: 2347-2355 - [c73]Nadav Torem, Roi Ronen, Yoav Y. Schechner, Michael Elad:
Complex-Valued Retrievals From Noisy Images Using Diffusion Models. ICCV (Workshops) 2023: 3812-3822 - [c72]Roy Ganz, Bahjat Kawar, Michael Elad:
Do Perceptually Aligned Gradients Imply Robustness? ICML 2023: 10628-10648 - [c71]Guy Ohayon, Theo Joseph Adrai, Michael Elad, Tomer Michaeli:
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality. ICML 2023: 26474-26494 - [c70]George Leifman, Idan Kligvasser, Roman Goldenberg, Ehud Rivlin, Michael Elad:
Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time. CaPTion@MICCAI 2023: 107-118 - [c69]Niranjan Sridhar, Michael Elad, Carson McNeil, Ehud Rivlin, Daniel Freedman:
Diffusion Models for Generative Histopathology. DGM4MICCAI 2023: 154-163 - [c68]Theo Adrai, Guy Ohayon, Michael Elad, Tomer Michaeli:
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration. NeurIPS 2023 - [c67]Gilad Kutiel, Regev Cohen, Michael Elad, Daniel Freedman, Ehud Rivlin:
Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging. TML4H 2023: 163-176 - [i91]Michael Elad, Bahjat Kawar, Gregory Vaksman:
Image Denoising: The Deep Learning Revolution and Beyond - A Survey Paper -. CoRR abs/2301.03362 (2023) - [i90]Guy Bar-Shalom, George Leifman, Michael Elad, Ehud Rivlin:
Weakly-supervised Representation Learning for Video Alignment and Analysis. CoRR abs/2302.04064 (2023) - [i89]Tsachi Blau, Roy Ganz, Chaim Baskin, Michael Elad, Alexander M. Bronstein:
Classifier Robustness Enhancement Via Test-Time Transformation. CoRR abs/2303.15409 (2023) - [i88]George Leifman, Idan Kligvasser, Roman Goldenberg, Michael Elad, Ehud Rivlin:
Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time. CoRR abs/2305.10026 (2023) - [i87]Idan Kligvasser, George Leifman, Roman Goldenberg, Ehud Rivlin, Michael Elad:
Semi-supervised Quality Evaluation of Colonoscopy Procedures. CoRR abs/2305.10090 (2023) - [i86]Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad:
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems. CoRR abs/2305.10124 (2023) - [i85]Bahjat Kawar, Noam Elata, Tomer Michaeli, Michael Elad:
GSURE-Based Diffusion Model Training with Corrupted Data. CoRR abs/2305.13128 (2023) - [i84]Noam Elata, Bahjat Kawar, Tomer Michaeli, Michael Elad:
Nested Diffusion Processes for Anytime Image Generation. CoRR abs/2305.19066 (2023) - [i83]Theo Adrai, Guy Ohayon, Tomer Michaeli, Michael Elad:
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration. CoRR abs/2306.02342 (2023) - [i82]Roy Ganz, Michael Elad:
CLIPAG: Towards Generator-Free Text-to-Image Generation. CoRR abs/2306.16805 (2023) - [i81]Roi Benita, Michael Elad, Joseph Keshet:
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform Generation. CoRR abs/2310.01381 (2023) - [i80]Guy Ohayon, Tomer Michaeli, Michael Elad:
The Perception-Robustness Tradeoff in Deterministic Image Restoration. CoRR abs/2311.09253 (2023) - 2022
- [j120]Aviad Aberdam, Alona Golts, Michael Elad:
Ada-LISTA: Learned Solvers Adaptive to Varying Models. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9222-9235 (2022) - [c66]Alona Golts, Ido Livneh, Yaniv Zohar, Aaron Ciechanover, Michael Elad:
Simultaneous Detection and Classification of Partially and Weakly Supervised Cells. ECCV Workshops (3) 2022: 313-329 - [c65]Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song:
Denoising Diffusion Restoration Models. NeurIPS 2022 - [i79]Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song:
Denoising Diffusion Restoration Models. CoRR abs/2201.11793 (2022) - [i78]Tsachi Blau, Roy Ganz, Bahjat Kawar, Alex M. Bronstein, Michael Elad:
Threat Model-Agnostic Adversarial Defense using Diffusion Models. CoRR abs/2207.08089 (2022) - [i77]Roy Ganz, Bahjat Kawar, Michael Elad:
Do Perceptually Aligned Gradients Imply Adversarial Robustness? CoRR abs/2207.11378 (2022) - [i76]Bahjat Kawar, Roy Ganz, Michael Elad:
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. CoRR abs/2208.08664 (2022) - [i75]Bahjat Kawar, Jiaming Song, Stefano Ermon, Michael Elad:
JPEG Artifact Correction using Denoising Diffusion Restoration Models. CoRR abs/2209.11888 (2022) - [i74]Guy Ohayon, Theo Adrai, Michael Elad, Tomer Michaeli:
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality. CoRR abs/2211.08944 (2022) - [i73]Gregory Vaksman, Michael Elad:
Patch-Craft Self-Supervised Training for Correlated Image Denoising. CoRR abs/2211.09919 (2022) - [i72]Sean Man, Guy Ohayon, Theo Adrai, Michael Elad:
High-Perceptual Quality JPEG Decoding via Posterior Sampling. CoRR abs/2211.11827 (2022) - [i71]Gilad Kutiel, Regev Cohen, Michael Elad, Daniel Freedman:
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems. CoRR abs/2211.15211 (2022) - [i70]Nadav Torem, Roi Ronen, Yoav Y. Schechner, Michael Elad:
Towards A Most Probable Recovery in Optical Imaging. CoRR abs/2212.03235 (2022) - 2021
- [j119]Alona Golts, Daniel Freedman, Michael Elad:
Deep Energy: Task Driven Training of Deep Neural Networks. IEEE J. Sel. Top. Signal Process. 15(2): 324-338 (2021) - [j118]Rajaei Khatib, Dror Simon, Michael Elad:
Learned Greedy Method (LGM): A novel neural architecture for sparse coding and beyond. J. Vis. Commun. Image Represent. 77: 103095 (2021) - [j117]Regev Cohen, Michael Elad, Peyman Milanfar:
Regularization by Denoising via Fixed-Point Projection (RED-PRO). SIAM J. Imaging Sci. 14(3): 1374-1406 (2021) - [j116]Meyer Scetbon, Michael Elad, Peyman Milanfar:
Deep K-SVD Denoising. IEEE Trans. Image Process. 30: 5944-5955 (2021) - [j115]Hossein Talebi Esfandarani, Damien Kelly, Xiyang Luo, Ignacio Garcia-Dorado, Feng Yang, Peyman Milanfar, Michael Elad:
Better Compression With Deep Pre-Editing. IEEE Trans. Image Process. 30: 6673-6685 (2021) - [c64]Xiyang Luo, Hossein Talebi, Feng Yang, Michael Elad, Peyman Milanfar:
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study. DCC 2021: 354 - [c63]Gregory Vaksman, Michael Elad, Peyman Milanfar:
Patch Craft: Video Denoising by Deep Modeling and Patch Matching. ICCV 2021: 2137-2146 - [c62]Guy Ohayon, Theo Adrai, Gregory Vaksman, Michael Elad, Peyman Milanfar:
High Perceptual Quality Image Denoising with a Posterior Sampling CGAN. ICCVW 2021: 1805-1813 - [c61]Bahjat Kawar, Gregory Vaksman, Michael Elad:
Stochastic Image Denoising by Sampling from the Posterior Distribution. ICCVW 2021: 1866-1875 - [c60]Bahjat Kawar, Gregory Vaksman, Michael Elad:
SNIPS: Solving Noisy Inverse Problems Stochastically. NeurIPS 2021: 21757-21769 - [e4]Yuxin Peng, Shi-Min Hu, Moncef Gabbouj, Kun Zhou, Michael Elad, Kun Xu:
Image and Graphics - 11th International Conference, ICIG 2021, Haikou, China, August 6-8, 2021, Proceedings, Part I. Lecture Notes in Computer Science 12888, Springer 2021, ISBN 978-3-030-87354-7 [contents] - [e3]Yuxin Peng, Shi-Min Hu, Moncef Gabbouj, Kun Zhou, Michael Elad, Kun Xu:
Image and Graphics - 11th International Conference, ICIG 2021, Haikou, China, August 6-8, 2021, Proceedings, Part II. Lecture Notes in Computer Science 12889, Springer 2021, ISBN 978-3-030-87357-8 [contents] - [e2]Yuxin Peng, Shi-Min Hu, Moncef Gabbouj, Kun Zhou, Michael Elad, Kun Xu:
Image and Graphics - 11th International Conference, ICIG 2021, Haikou, China, August 6-8, 2021, Proceedings, Part III. Lecture Notes in Computer Science 12890, Springer 2021, ISBN 978-3-030-87360-8 [contents] - [i69]Bahjat Kawar, Gregory Vaksman, Michael Elad:
Stochastic Image Denoising by Sampling from the Posterior Distribution. CoRR abs/2101.09552 (2021) - [i68]Guy Ohayon, Theo Adrai, Gregory Vaksman, Michael Elad, Peyman Milanfar:
High Perceptual Quality Image Denoising with a Posterior Sampling CGAN. CoRR abs/2103.04192 (2021) - [i67]Gregory Vaksman, Michael Elad, Peyman Milanfar:
Patch Craft: Video Denoising by Deep Modeling and Patch Matching. CoRR abs/2103.13767 (2021) - [i66]Roy Ganz, Michael Elad:
Improved Image Generation via Sparse Modeling. CoRR abs/2104.00464 (2021) - [i65]Bahjat Kawar, Gregory Vaksman, Michael Elad:
SNIPS: Solving Noisy Inverse Problems Stochastically. CoRR abs/2105.14951 (2021) - [i64]Roy Ganz, Michael Elad:
BIGRoC: Boosting Image Generation via a Robust Classifier. CoRR abs/2108.03702 (2021) - 2020
- [j114]Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad:
Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals. J. Math. Imaging Vis. 62(3): 313-327 (2020) - [j113]Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad:
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8): 1968-1980 (2020) - [j112]Alona Golts, Daniel Freedman, Michael Elad:
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss. IEEE Trans. Image Process. 29: 2692-2701 (2020) - [j111]Ives Rey-Otero, Jeremias Sulam, Michael Elad:
Variations on the Convolutional Sparse Coding Model. IEEE Trans. Signal Process. 68: 519-528 (2020) - [c59]Gregory Vaksman, Michael Elad, Peyman Milanfar:
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation. CVPR Workshops 2020: 2220-2229 - [i63]Aviad Aberdam, Alona Golts, Michael Elad:
Ada-LISTA: Learned Solvers Adaptive to Varying Models. CoRR abs/2001.08456 (2020) - [i62]Hossein Talebi, Damien Kelly, Xiyang Luo, Ignacio Garcia-Dorado, Feng Yang, Peyman Milanfar, Michael Elad:
Better Compression with Deep Pre-Editing. CoRR abs/2002.00113 (2020) - [i61]Aviad Aberdam, Dror Simon, Michael Elad:
When and How Can Deep Generative Models be Inverted? CoRR abs/2006.15555 (2020) - [i60]Regev Cohen, Michael Elad, Peyman Milanfar:
Regularization by Denoising via Fixed-Point Projection (RED-PRO). CoRR abs/2008.00226 (2020) - [i59]Xiyang Luo, Hossein Talebi, Feng Yang, Michael Elad, Peyman Milanfar:
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study. CoRR abs/2008.00605 (2020) - [i58]Rajaei Khatib, Dror Simon, Michael Elad:
Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond. CoRR abs/2010.07069 (2020)
2010 – 2019
- 2019
- [j110]Tao Hong, Yaniv Romano, Michael Elad:
Acceleration of RED via vector extrapolation. J. Vis. Commun. Image Represent. 63 (2019) - [j109]Aviad Aberdam, Jeremias Sulam, Michael Elad:
Multi-Layer Sparse Coding: The Holistic Way. SIAM J. Math. Data Sci. 1(1): 46-77 (2019) - [j108]Alon Brifman, Yaniv Romano, Michael Elad:
Unified Single-Image and Video Super-Resolution via Denoising Algorithms. IEEE Trans. Image Process. 28(12): 6063-6076 (2019) - [j107]Yael Yankelevsky, Michael Elad:
Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals. IEEE Trans. Signal Process. 67(7): 1889-1901 (2019) - [j106]Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad:
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance. IEEE Trans. Signal Process. 67(17): 4597-4610 (2019) - [c58]Ev Zisselman, Jeremias Sulam, Michael Elad:
A Local Block Coordinate Descent Algorithm for the CSC Model. CVPR 2019: 8208-8217 - [c57]Shahar Romem Peled, Yaniv Romano, Michael Elad:
SOS Boosting for Image Deblurring Algorithms. EUSIPCO 2019: 1-5 - [c56]Dror Simon, Michael Elad:
Rethinking the CSC Model for Natural Images. NeurIPS 2019: 2271-2281 - [i57]Gary Mataev, Michael Elad, Peyman Milanfar:
DeepRED: Deep Image Prior Powered by RED. CoRR abs/1903.10176 (2019) - [i56]Dror Simon, Michael Elad:
Rethinking the CSC Model for Natural Images. CoRR abs/1909.05742 (2019) - [i55]Meyer Scetbon, Michael Elad, Peyman Milanfar:
Deep K-SVD Denoising. CoRR abs/1909.13164 (2019) - [i54]Gregory Vaksman, Michael Elad, Peyman Milanfar:
Low-Weight and Learnable Image Denoising. CoRR abs/1911.07167 (2019) - 2018
- [j105]Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad:
Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks. IEEE Signal Process. Mag. 35(4): 72-89 (2018) - [j104]Yi Ren, Yaniv Romano, Michael Elad:
Example-Based Image Synthesis via Randomized Patch-Matching. IEEE Trans. Image Process. 27(1): 220-235 (2018) - [j103]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
Optimized Pre-Compensating Compression. IEEE Trans. Image Process. 27(10): 4798-4809 (2018) - [j102]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. IEEE Trans. Signal Process. 66(15): 4090-4104 (2018) - [j101]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
Restoration by Compression. IEEE Trans. Signal Process. 66(22): 5833-5847 (2018) - [c55]Yael Yankelevsky, Michael Elad:
Dictionary Learning for High Dimensional Graph Signals. ICASSP 2018: 4669-4673 - [c54]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Projecting on to the Multi-Layer Convolutional Sparse Coding Model. ICASSP 2018: 6757-6761 - [c53]Yaniv Romano, Michael Elad, Peyman Milanfar:
RED-UCATION: A Novel CNN Architecture Based on Denoising Nonlinearities. ICASSP 2018: 6762-6766 - [c52]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
Compression for Multiple Reconstructions. ICIP 2018: 440-444 - [c51]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
System-Aware Compression. ISIT 2018: 2226-2230 - [i53]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
System-Aware Compression. CoRR abs/1801.04853 (2018) - [i52]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
Compression for Multiple Reconstructions. CoRR abs/1802.03937 (2018) - [i51]Aviad Aberdam, Jeremias Sulam, Michael Elad:
Multi Layer Sparse Coding: the Holistic Way. CoRR abs/1804.09788 (2018) - [i50]Tao Hong, Yaniv Romano, Michael Elad:
Acceleration of RED via Vector Extrapolation. CoRR abs/1805.02158 (2018) - [i49]Yaniv Romano, Michael Elad:
Classification Stability for Sparse-Modeled Signals. CoRR abs/1805.11596 (2018) - [i48]Alona Golts, Daniel Freedman, Michael Elad:
Deep Energy: Using Energy Functions for Unsupervised Training of DNNs. CoRR abs/1805.12355 (2018) - [i47]Jeremias Sulam, Aviad Aberdam, Michael Elad:
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. CoRR abs/1806.00701 (2018) - [i46]Yael Yankelevsky, Michael Elad:
Finding GEMS: Multi-Scale Dictionaries for High-Dimensional Graph Signals. CoRR abs/1806.05356 (2018) - [i45]Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad:
Improving Pursuit Algorithms Using Stochastic Resonance. CoRR abs/1806.10171 (2018) - [i44]Ev Zisselman, Jeremias Sulam, Michael Elad:
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model. CoRR abs/1811.00312 (2018) - [i43]Alona Golts, Daniel Freedman, Michael Elad:
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss. CoRR abs/1812.07051 (2018) - 2017
- [j100]Vardan Papyan, Yaniv Romano, Michael Elad:
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding. J. Mach. Learn. Res. 18: 83:1-83:52 (2017) - [j99]Yaniv Romano, Michael Elad, Peyman Milanfar:
The Little Engine That Could: Regularization by Denoising (RED). SIAM J. Imaging Sci. 10(4): 1804-1844 (2017) - [j98]Michael Elad, Peyman Milanfar:
Style Transfer Via Texture Synthesis. IEEE Trans. Image Process. 26(5): 2338-2351 (2017) - [j97]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. IEEE Trans. Signal Process. 65(21): 5687-5701 (2017) - [c50]Yael Yankelevsky, Michael Elad:
Structure-aware classification using supervised dictionary learning. ICASSP 2017: 4421-4425 - [c49]Vardan Papyan, Yaniv Romano, Michael Elad, Jeremias Sulam:
Convolutional Dictionary Learning via Local Processing. ICCV 2017: 5306-5314 - [i42]Dmitry Batenkov, Yaniv Romano, Michael Elad:
On the Global-Local Dichotomy in Sparsity Modeling. CoRR abs/1702.03446 (2017) - [i41]Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad:
Convolutional Dictionary Learning via Local Processing. CoRR abs/1705.03239 (2017) - [i40]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. CoRR abs/1707.06066 (2017) - [i39]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. CoRR abs/1708.08705 (2017) - [i38]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
Restoration by Compression. CoRR abs/1711.05147 (2017) - [i37]Yehuda Dar, Michael Elad, Alfred M. Bruckstein:
Optimized Pre-Compensating Compression. CoRR abs/1711.07901 (2017) - 2016
- [j96]Alona Golts, Michael Elad:
Linearized Kernel Dictionary Learning. IEEE J. Sel. Top. Signal Process. 10(4): 726-739 (2016) - [j95]Arie Rond, Raja Giryes, Michael Elad:
Poisson inverse problems by the Plug-and-Play scheme. J. Vis. Commun. Image Represent. 41: 96-108 (2016) - [j94]Gregory Vaksman, Michael Zibulevsky, Michael Elad:
Patch Ordering as a Regularization for Inverse Problems in Image Processing. SIAM J. Imaging Sci. 9(1): 287-319 (2016) - [j93]Jeremias Sulam, Michael Elad:
Large Inpainting of Face Images With Trainlets. IEEE Signal Process. Lett. 23(12): 1839-1843 (2016) - [j92]Vardan Papyan, Michael Elad:
Multi-Scale Patch-Based Image Restoration. IEEE Trans. Image Process. 25(1): 249-261 (2016) - [j91]Yehuda Dar, Alfred M. Bruckstein, Michael Elad, Raja Giryes:
Postprocessing of Compressed Images via Sequential Denoising. IEEE Trans. Image Process. 25(7): 3044-3058 (2016) - [j90]Yaniv Romano, Michael Elad:
Con-Patch: When a Patch Meets Its Context. IEEE Trans. Image Process. 25(9): 3967-3978 (2016) - [j89]Yael Yankelevsky, Michael Elad:
Dual Graph Regularized Dictionary Learning. IEEE Trans. Signal Inf. Process. over Networks 2(4): 611-624 (2016) - [j88]Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad:
Trainlets: Dictionary Learning in High Dimensions. IEEE Trans. Signal Process. 64(12): 3180-3193 (2016) - [c48]Alon Brifman, Yaniv Romano, Michael Elad:
Turning a denoiser into a super-resolver using plug and play priors. ICIP 2016: 1404-1408 - [c47]Yehuda Dar, Alfred M. Bruckstein, Michael Elad:
Image restoration via successive compression. PCS 2016: 1-5 - [i36]Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad:
Trainlets: Dictionary Learning in High Dimensions. CoRR abs/1602.00212 (2016) - [i35]Gregory Vaksman, Michael Zibulevsky, Michael Elad:
Patch-Ordering as a Regularization for Inverse Problems in Image Processing. CoRR abs/1602.08510 (2016) - [i34]Yaniv Romano, Michael Elad:
Con-Patch: When a Patch Meets its Context. CoRR abs/1603.06812 (2016) - [i33]Amir Adler, David Boublil, Michael Elad, Michael Zibulevsky:
A Deep Learning Approach to Block-based Compressed Sensing of Images. CoRR abs/1606.01519 (2016) - [i32]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding. CoRR abs/1607.02005 (2016) - [i31]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding. CoRR abs/1607.02009 (2016) - [i30]Vardan Papyan, Yaniv Romano, Michael Elad:
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding. CoRR abs/1607.08194 (2016) - [i29]Michael Elad, Peyman Milanfar:
Style-Transfer via Texture-Synthesis. CoRR abs/1609.03057 (2016) - [i28]Yi Ren, Yaniv Romano, Michael Elad:
Example-Based Image Synthesis via Randomized Patch-Matching. CoRR abs/1609.07370 (2016) - [i27]Yael Yankelevsky, Michael Elad:
Structure-Aware Classification using Supervised Dictionary Learning. CoRR abs/1609.09199 (2016) - [i26]Amir Adler, Michael Elad, Michael Zibulevsky:
Compressed Learning: A Deep Neural Network Approach. CoRR abs/1610.09615 (2016) - [i25]Yaniv Romano, Michael Elad, Peyman Milanfar:
The Little Engine that Could: Regularization by Denoising (RED). CoRR abs/1611.02862 (2016) - 2015
- [j87]Javier S. Turek, Michael Elad, Irad Yavneh:
Clutter Mitigation in Echocardiography Using Sparse Signal Separation. Int. J. Biomed. Imaging 2015: 958963:1-958963:18 (2015) - [j86]Julien Mairal, Michael Elad, Francis R. Bach:
Guest Editorial: Sparse Coding. Int. J. Comput. Vis. 114(2-3): 89-90 (2015) - [j85]Wen-Ze Shao, Haibo Li, Michael Elad:
Bi-l0-l2-norm regularization for blind motion deblurring. J. Vis. Commun. Image Represent. 33: 42-59 (2015) - [j84]Yaniv Romano, Michael Elad:
Boosting of Image Denoising Algorithms. SIAM J. Imaging Sci. 8(2): 1187-1219 (2015) - [j83]Raja Giryes, Michael Elad, Alfred M. Bruckstein:
Sparsity Based Methods for Overparameterized Variational Problems. SIAM J. Imaging Sci. 8(3): 2133-2159 (2015) - [j82]Yuval Bahat, Yoav Y. Schechner, Michael Elad:
Self-content-based audio inpainting. Signal Process. 111: 61-72 (2015) - [j81]David Boublil, Michael Elad, Joseph Shtok, Michael Zibulevsky:
Spatially-Adaptive Reconstruction in Computed Tomography Using Neural Networks. IEEE Trans. Medical Imaging 34(7): 1474-1485 (2015) - [j80]Amir Adler, Michael Elad, Yacov Hel-Or:
Linear-Time Subspace Clustering via Bipartite Graph Modeling. IEEE Trans. Neural Networks Learn. Syst. 26(10): 2234-2246 (2015) - [j79]Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin:
Sparse Coding with Anomaly Detection. J. Signal Process. Syst. 79(2): 179-188 (2015) - [c46]Javier S. Turek, Jeremias Sulam, Michael Elad, Irad Yavneh:
Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation. ICASSP 2015: 793-797 - [c45]Yaniv Romano, Michael Elad:
Patch-disagreement as away to improve K-SVD denoising. ICASSP 2015: 1280-1284 - [c44]Wenze Shao, Michael Elad:
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution. ICIG (3) 2015: 333-348 - [c43]Esben Plenge, Mitchell A. Cooper, Martin R. Prince, Yi Wang, Pascal Spincemaille, Michael Elad:
Reconstruction of highly under-sampled dynamic MRI using sparse representation of 1D temporal snippets. ISBI 2015: 1240-1243 - [i24]Yaniv Romano, Michael Elad:
SOS Boosting of Image Denoising Algorithms. CoRR abs/1502.06220 (2015) - [i23]Wen-Ze Shao, Michael Elad:
Simple, Accurate, and Robust Nonparametric Blind Super-Resolution. CoRR abs/1503.03187 (2015) - [i22]Alona Golts, Michael Elad:
Linearized Kernel Dictionary Learning. CoRR abs/1509.05634 (2015) - [i21]Yehuda Dar, Alfred M. Bruckstein, Michael Elad, Raja Giryes:
Postprocessing of Compressed Images via Sequential Denoising. CoRR abs/1510.09041 (2015) - [i20]Arie Rond, Raja Giryes, Michael Elad:
Poisson Inverse Problems by the Plug-and-Play scheme. CoRR abs/1511.02500 (2015) - 2014
- [j78]Javier Turek, Irad Yavneh, Michael Elad:
On MAP and MMSE estimators for the co-sparse analysis model. Digit. Signal Process. 28: 57-74 (2014) - [j77]Idan Ram, Israel Cohen, Michael Elad:
Facial Image Compression using Patch-Ordering-Based Adaptive Wavelet Transform. IEEE Signal Process. Lett. 21(10): 1270-1274 (2014) - [j76]Tomer Peleg, Michael Elad:
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution. IEEE Trans. Image Process. 23(6): 2569-2582 (2014) - [j75]Idan Ram, Israel Cohen, Michael Elad:
Patch-Ordering-Based Wavelet Frame and Its Use in Inverse Problems. IEEE Trans. Image Process. 23(7): 2779-2792 (2014) - [j74]Yaniv Romano, Matan Protter, Michael Elad:
Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling. IEEE Trans. Image Process. 23(7): 3085-3098 (2014) - [j73]Raja Giryes, Michael Elad:
Sparsity-Based Poisson Denoising With Dictionary Learning. IEEE Trans. Image Process. 23(12): 5057-5069 (2014) - [j72]Ron Rubinstein, Michael Elad:
Dictionary Learning for Analysis-Synthesis Thresholding. IEEE Trans. Signal Process. 62(22): 5962-5972 (2014) - [c42]Jeremias Sulam, Michael Elad:
Expected Patch Log Likelihood with a Sparse Prior. EMMCVPR 2014: 99-111 - [c41]Jeremias Sulam, Boaz Ophir, Michael Elad:
Image denoising through multi-scale learnt dictionaries. ICIP 2014: 808-812 - [c40]Raja Giryes, Michael Elad:
Sparsity based poisson inpainting. ICIP 2014: 2839-2843 - [i19]Raja Giryes, Michael Elad, Alfred M. Bruckstein:
Sparsity Based Methods for Overparametrized Variational Problems. CoRR abs/1405.4969 (2014) - [i18]Wen-Ze Shao, Haibo Li, Michael Elad:
Bi-l0-l2-Norm Regularization for Blind Motion Deblurring. CoRR abs/1408.4712 (2014) - 2013
- [j71]Joseph Shtok, Michael Elad, Michael Zibulevsky:
Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography. Int. J. Biomed. Imaging 2013: 609274:1-609274:20 (2013) - [j70]Amir Adler, Michael Elad, Yacov Hel-Or:
Probabilistic Subspace Clustering Via Sparse Representations. IEEE Signal Process. Lett. 20(1): 63-66 (2013) - [j69]Leslie N. Smith, Michael Elad:
Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse. IEEE Signal Process. Lett. 20(1): 79-82 (2013) - [j68]Idan Ram, Michael Elad, Israel Cohen:
Image Processing Using Smooth Ordering of its Patches. IEEE Trans. Image Process. 22(7): 2764-2774 (2013) - [j67]Tomer Peleg, Michael Elad:
Performance Guarantees of the Thresholding Algorithm for the Cosparse Analysis Model. IEEE Trans. Inf. Theory 59(3): 1832-1845 (2013) - [j66]Ron Rubinstein, Tomer Peleg, Michael Elad:
Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model. IEEE Trans. Signal Process. 61(3): 661-677 (2013) - [c39]Idan Ram, Michael Elad, Israel Cohen:
Image denoising using NL-means via smooth patch ordering. ICASSP 2013: 1350-1354 - [c38]Raja Giryes, Michael Elad:
Can we allow linear dependencies in the dictionary in the sparse synthesis framework? ICASSP 2013: 5459-5463 - [c37]Yaniv Romano, Michael Elad:
Improving K-SVD denoising by post-processing its method-noise. ICIP 2013: 435-439 - [c36]Amir Adler, Michael Elad, Yacov Hel-Or, Ehud Rivlin:
Sparse coding with anomaly detection. MLSP 2013: 1-6 - [i17]Raja Giryes, Michael Elad:
Can we allow linear dependencies in the dictionary in the sparse synthesis framework? CoRR abs/1303.5655 (2013) - [i16]Raja Giryes, Michael Elad:
Sparsity Based Poisson Denoising with Dictionary Learning. CoRR abs/1309.4306 (2013) - [i15]Joseph Shtok, Michael Zibulevsky, Michael Elad:
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks. CoRR abs/1311.7251 (2013) - 2012
- [j65]Idan Ram, Michael Elad, Israel Cohen:
Redundant Wavelets on Graphs and High Dimensional Data Clouds. IEEE Signal Process. Lett. 19(5): 291-294 (2012) - [j64]Michael Elad:
Sparse and Redundant Representation Modeling - What Next? IEEE Signal Process. Lett. 19(12): 922-928 (2012) - [j63]Amir Adler, Valentin Emiya, Maria G. Jafari, Michael Elad, Rémi Gribonval, Mark D. Plumbley:
Audio Inpainting. IEEE Trans. Speech Audio Process. 20(3): 922-932 (2012) - [j62]Raja Giryes, Michael Elad:
RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT. IEEE Trans. Signal Process. 60(3): 1465-1468 (2012) - [j61]Tomer Peleg, Yonina C. Eldar, Michael Elad:
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery. IEEE Trans. Signal Process. 60(5): 2286-2303 (2012) - [c35]Dana Segev, Yoav Y. Schechner, Michael Elad:
Example-based cross-modal denoising. CVPR 2012: 486-493 - [c34]Raja Giryes, Michael Elad:
Cosamp and SP for the cosparse analysis model. EUSIPCO 2012: 964-968 - [c33]Ron Rubinstein, Tomer Faktor, Michael Elad:
K-SVD dictionary-learning for the analysis sparse model. ICASSP 2012: 5405-5408 - [i14]Tomer Peleg, Michael Elad:
Performance Guarantees of the Thresholding Algorithm for the Co-Sparse Analysis Model. CoRR abs/1203.2769 (2012) - [i13]Idan Ram, Michael Elad, Israel Cohen:
Image Processing using Smooth Ordering of its Patches. CoRR abs/1210.3832 (2012) - 2011
- [j60]Jean-Luc Starck, Jalal Fadili, Michael Elad, Robert D. Nowak, Panagiotis Tsakalides:
Introduction to the issue on Adaptive Sparse Representation of Data and Applications in Signal and Image Processing. IEEE J. Sel. Top. Signal Process. 5(5): 893-895 (2011) - [j59]Boaz Ophir, Michael Lustig, Michael Elad:
Multi-Scale Dictionary Learning Using Wavelets. IEEE J. Sel. Top. Signal Process. 5(5): 1014-1024 (2011) - [j58]Javier Turek, Irad Yavneh, Michael Elad:
On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary. IEEE Trans. Signal Process. 59(8): 3526-3535 (2011) - [j57]Idan Ram, Michael Elad, Israel Cohen:
Generalized Tree-Based Wavelet Transform. IEEE Trans. Signal Process. 59(9): 4199-4209 (2011) - [c32]Sangnam Nam, Michael E. Davies, Michael Elad, Rémi Gribonval:
Recovery of cosparse signals with Greedy Analysis Pursuit in the presence of noise. CAMSAP 2011: 361-364 - [c31]Boaz Ophir, Michael Elad, Nancy Bertin, Mark D. Plumbley:
Sequential minimal eigenvalues - an approach to analysis dictionary learning. EUSIPCO 2011: 1465-1469 - [c30]Raja Giryes, Michael Elad:
Denoising with greedy-like pursuit algorithms. EUSIPCO 2011: 1475-1479 - [c29]Amir Adler, Valentin Emiya, Maria G. Jafari, Michael Elad, Rémi Gribonval, Mark D. Plumbley:
A constrained matching pursuit approach to audio declipping. ICASSP 2011: 329-332 - [c28]Joseph Shtok, Michael Elad, Michael Zibulevsky:
Sparsity-based Sinogram Denoising for low-dose Computed Tomography. ICASSP 2011: 569-572 - [c27]Sangnam Nam, Michael E. Davies, Michael Elad, Rémi Gribonval:
Cosparse analysis modeling - uniqueness and algorithms. ICASSP 2011: 5804-5807 - [c26]Tomer Faktor, Yonina C. Eldar, Michael Elad:
Denoising of image patches via sparse representations with learned statistical dependencies. ICASSP 2011: 5820-5823 - [i12]Sangnam Nam, Mike E. Davies, Michael Elad, Rémi Gribonval:
The Cosparse Analysis Model and Algorithms. CoRR abs/1106.4987 (2011) - [i11]Idan Ram, Michael Elad, Israel Cohen:
Redundant Wavelets on Graphs and High Dimensional Data Clouds. CoRR abs/1111.4619 (2011) - [i10]Stephan Dahlke, Michael Elad, Yonina C. Eldar, Gitta Kutyniok, Gerd Teschke:
Sparse Representations and Efficient Sensing of Data (Dagstuhl Seminar 11051). Dagstuhl Reports 1(1): 108-127 (2011) - 2010
- [b1]Michael Elad:
Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing. Springer 2010, ISBN 978-1-4419-7010-7, pp. I-XX, 1-376 - [j56]Michael Elad, Raja Giryes:
Iterative signal recovery from incomplete samples: technical perspective. Commun. ACM 53(12): 92 (2010) - [j55]Michael Elad, Dmitry Datsenko:
Corrigendum: Example-Based Regularization Deployed to Super-Resolution Reconstruction of a Single Image. Comput. J. 53(7): 1131 (2010) - [j54]Mohamed-Jalal Fadili, Jean-Luc Starck, Michael Elad, David L. Donoho:
MCALab: Reproducible Research in Signal and Image Decomposition and Inpainting. Comput. Sci. Eng. 12(1): 44-63 (2010) - [j53]Richard G. Baraniuk, Emmanuel J. Candès, Michael Elad, Yi Ma:
Applications of Sparse Representation and Compressive Sensing. Proc. IEEE 98(6): 906-909 (2010) - [j52]Michael Elad, Mário A. T. Figueiredo, Yi Ma:
On the Role of Sparse and Redundant Representations in Image Processing. Proc. IEEE 98(6): 972-982 (2010) - [j51]Ron Rubinstein, Alfred M. Bruckstein, Michael Elad:
Dictionaries for Sparse Representation Modeling. Proc. IEEE 98(6): 1045-1057 (2010) - [j50]Michael Zibulevsky, Michael Elad:
L1-L2 Optimization in Signal and Image Processing. IEEE Signal Process. Mag. 27(3): 76-88 (2010) - [j49]Ron Rubinstein, Michael Zibulevsky, Michael Elad:
Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans. Signal Process. 58(3): 1553-1564 (2010) - [j48]Matan Protter, Irad Yavneh, Michael Elad:
Closed-form MMSE estimation for signal denoising under sparse representation modeling over a unitary dictionary. IEEE Trans. Signal Process. 58(7): 3471-3484 (2010) - [j47]Zvika Ben-Haim, Yonina C. Eldar, Michael Elad:
Coherence-based performance guarantees for estimating a sparse vector under random noise. IEEE Trans. Signal Process. 58(10): 5030-5043 (2010) - [c25]Roman Zeyde, Michael Elad, Matan Protter:
On Single Image Scale-Up Using Sparse-Representations. Curves and Surfaces 2010: 711-730 - [c24]Amir Adler, Yacov Hel-Or, Michael Elad:
A Shrinkage Learning Approach for Single Image Super-Resolution with Overcomplete Representations. ECCV (2) 2010: 622-635 - [c23]Amir Adler, Yacov Hel-Or, Michael Elad:
A weighted discriminative approach for image denoising with overcomplete representations. ICASSP 2010: 782-785 - [c22]Zvika Ben-Haim, Yonina C. Eldar, Michael Elad:
Coherence-based near-oracle performance guarantees for sparse estimation under Gaussian noise. ICASSP 2010: 3590-3593 - [i9]Javier Turek, Irad Yavneh, Matan Protter, Michael Elad:
On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary. CoRR abs/1003.3984 (2010) - [i8]Raja Giryes, Michael Elad, Yonina C. Eldar:
The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods. CoRR abs/1003.3985 (2010) - [i7]Joseph Shtok, Michael Elad:
Analysis of Basis Pursuit Via Capacity Sets. CoRR abs/1004.4329 (2010) - [i6]Joseph Shtok, Michael Zibulevsky, Michael Elad:
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning. CoRR abs/1004.4373 (2010) - [i5]Tomer Faktor, Yonina C. Eldar, Michael Elad:
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery. CoRR abs/1010.5734 (2010) - [i4]Idan Ram, Michael Elad, Israel Cohen:
Generalized Tree-Based Wavelet Transform. CoRR abs/1011.4615 (2010)
2000 – 2009
- 2009
- [j46]Michael Elad, Dmitry Datsenko:
Example-Based Regularization Deployed to Super-Resolution Reconstruction of a Single Image. Comput. J. 52(1): 15-30 (2009) - [j45]Alfred M. Bruckstein, David L. Donoho, Michael Elad:
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images. SIAM Rev. 51(1): 34-81 (2009) - [j44]Matan Protter, Michael Elad:
Image Sequence Denoising via Sparse and Redundant Representations. IEEE Trans. Image Process. 18(1): 27-35 (2009) - [j43]Matan Protter, Michael Elad, Hiroyuki Takeda, Peyman Milanfar:
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction. IEEE Trans. Image Process. 18(1): 36-51 (2009) - [j42]Matan Protter, Michael Elad:
Super Resolution With Probabilistic Motion Estimation. IEEE Trans. Image Process. 18(8): 1899-1904 (2009) - [j41]Hiroyuki Takeda, Peyman Milanfar, Matan Protter, Michael Elad:
Super-Resolution Without Explicit Subpixel Motion Estimation. IEEE Trans. Image Process. 18(9): 1958-1975 (2009) - [j40]Michael Elad, Irad Yavneh:
A plurality of sparse representations is better than the sparsest one alone. IEEE Trans. Inf. Theory 55(10): 4701-4714 (2009) - [c21]Joseph Shtok, Michael Elad, Michael Zibulevsky:
Direct Adaptive Algorithms for CT Reconstruction. ISBI 2009: 181-184 - 2008
- [j39]Ori Bryt, Michael Elad:
Compression of facial images using the K-SVD algorithm. J. Vis. Commun. Image Represent. 19(4): 270-282 (2008) - [j38]Julien Mairal, Guillermo Sapiro, Michael Elad:
Learning Multiscale Sparse Representations for Image and Video Restoration. Multiscale Model. Simul. 7(1): 214-241 (2008) - [j37]Michal Aharon, Michael Elad:
Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary. SIAM J. Imaging Sci. 1(3): 228-247 (2008) - [j36]Julien Mairal, Michael Elad, Guillermo Sapiro:
Sparse Representation for Color Image Restoration. IEEE Trans. Image Process. 17(1): 53-69 (2008) - [j35]Alfred M. Bruckstein, Michael Elad, Michael Zibulevsky:
On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations. IEEE Trans. Inf. Theory 54(11): 4813-4820 (2008) - [c20]Alfred M. Bruckstein, Michael Elad, Michael Zibulevsky:
On the uniqueness of non-negative sparse & redundant representations. ICASSP 2008: 5145-5148 - [e1]Stephan Dahlke, Ingrid Daubechies, Michael Elad, Gitta Kutyniok, Gerd Teschke:
Structured Decompositions and Efficient Algorithms, 30.11. - 05.12.2008. Dagstuhl Seminar Proceedings 08492, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2008 [contents] - [i3]Stephan Dahlke, Ingrid Daubechies, Michael Elad, Gitta Kutyniok, Gerd Teschke:
08492 Abstracts Collection - Structured Decompositions and Efficient Algorithms. Structured Decompositions and Efficient Algorithms 2008 - [i2]Stephan Dahlke, Ingrid Daubechies, Michael Elad, Gitta Kutyniok, Gerd Teschke:
08492 Executive Summary - Structured Decompositions and Efficient Algorithms. Structured Decompositions and Efficient Algorithms 2008 - [i1]Michael Elad, Irad Yavneh:
A Weighted Average of Sparse Representations is Better than the Sparsest One Alone. Structured Decompositions and Efficient Algorithms 2008 - 2007
- [j34]Carl Staelin, Michael Elad, Darryl Greig, Oded Shmueli, Marie Vans:
Biblio: automatic meta-data extraction. Int. J. Document Anal. Recognit. 10(2): 113-126 (2007) - [j33]David L. Donoho, Michael Elad, Vladimir N. Temlyakov:
On Lebesgue-type inequalities for greedy approximation. J. Approx. Theory 147(2): 185-195 (2007) - [j32]Dmitry Datsenko, Michael Elad:
Example-based single document image super-resolution: a global MAP approach with outlier rejection. Multidimens. Syst. Signal Process. 18(2-3): 103-121 (2007) - [j31]Michael Elad, Roman Goldenberg, Ron Kimmel:
Low Bit-Rate Compression of Facial Images. IEEE Trans. Image Process. 16(9): 2379-2383 (2007) - [j30]Einat Kidron, Yoav Y. Schechner, Michael Elad:
Cross-Modal Localization via Sparsity. IEEE Trans. Signal Process. 55(4): 1390-1404 (2007) - [j29]Michael Elad:
Optimized Projections for Compressed Sensing. IEEE Trans. Signal Process. 55(12): 5695-5702 (2007) - [c19]Julien Mairal, Guillermo Sapiro, Michael Elad:
Multiscale Sparse Image Representationwith Learned Dictionaries. ICIP (3) 2007: 105-108 - 2006
- [j28]Michael Elad:
Sparse Representations Are Most Likely to Be the Sparsest Possible. EURASIP J. Adv. Signal Process. 2006 (2006) - [j27]Sina Farsiu, Michael Elad, Peyman Milanfar:
Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences. EURASIP J. Adv. Signal Process. 2006 (2006) - [j26]David L. Donoho, Michael Elad:
On the stability of the basis pursuit in the presence of noise. Signal Process. 86(3): 511-532 (2006) - [j25]Jérôme Bobin, Yassir Moudden, Jean-Luc Starck, Michael Elad:
Morphological diversity and source separation. IEEE Signal Process. Lett. 13(7): 409-412 (2006) - [j24]Sina Farsiu, Michael Elad, Peyman Milanfar:
Multiframe demosaicing and super-resolution of color images. IEEE Trans. Image Process. 15(1): 141-159 (2006) - [j23]Michael Elad, Michal Aharon:
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries. IEEE Trans. Image Process. 15(12): 3736-3745 (2006) - [j22]David L. Donoho, Michael Elad, Vladimir N. Temlyakov:
Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans. Inf. Theory 52(1): 6-18 (2006) - [j21]Michael Elad:
Why Simple Shrinkage Is Still Relevant for Redundant Representations? IEEE Trans. Inf. Theory 52(12): 5559-5569 (2006) - [j20]Michal Aharon, Michael Elad, Alfred M. Bruckstein:
K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11): 4311-4322 (2006) - [c18]Michael R. Charest, Michael Elad, Peyman Milanfar:
A General Iterative Regularization Framework For Image Denoising. CISS 2006: 452-457 - [c17]Michael Elad, Michal Aharon:
Image Denoising Via Learned Dictionaries and Sparse representation. CVPR (1) 2006: 895-900 - [c16]Michael Elad, Boaz Matalon, Michael Zibulevsky:
Image Denoising with Shrinkage and Redundant Representations. CVPR (2) 2006: 1924-1931 - [c15]Michael Elad, Peyman Milanfar, Ron Rubinstein:
Analysis versus synthesis in signal priors. EUSIPCO 2006: 1-5 - 2005
- [j19]Michael Elad, Patrick C. Teo, Yacov Hel-Or:
On the Design of Filters for Gradient-Based Motion Estimation. J. Math. Imaging Vis. 23(3): 345-365 (2005) - [j18]Gregory Boutry, Michael Elad, Gene H. Golub, Peyman Milanfar:
The Generalized Eigenvalue Problem for Nonsquare Pencils Using a Minimal Perturbation Approach. SIAM J. Matrix Anal. Appl. 27(2): 582-601 (2005) - [j17]Yaakov Tsaig, Michael Elad, Peyman Milanfar, Gene H. Golub:
Variable projection for near-optimal filtering in low bit-rate block coders. IEEE Trans. Circuits Syst. Video Technol. 15(1): 154-160 (2005) - [j16]Ron Kimmel, Doron Shaked, Michael Elad, Irwin Sobel:
Space-dependent color gamut mapping: a variational approach. IEEE Trans. Image Process. 14(6): 796-803 (2005) - [j15]Jean-Luc Starck, Michael Elad, David L. Donoho:
Image Decomposition via the Combination of Sparse Representations and a Variational Approach. IEEE Trans. Image Process. 14(10): 1570-1582 (2005) - [c14]Einat Kidron, Yoav Y. Schechner, Michael Elad:
Pixels that Sound. CVPR (1) 2005: 88-95 - [c13]Michael Elad:
Retinex by Two Bilateral Filters. Scale-Space 2005: 217-229 - 2004
- [j14]Sina Farsiu, M. Dirk Robinson, Michael Elad, Peyman Milanfar:
Advances and challenges in super-resolution. Int. J. Imaging Syst. Technol. 14(2): 47-57 (2004) - [j13]Sina Farsiu, M. Dirk Robinson, Michael Elad, Peyman Milanfar:
Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10): 1327-1344 (2004) - [j12]Michael Elad, Peyman Milanfar, Gene H. Golub:
Shape from moments - an estimation theory perspective. IEEE Trans. Signal Process. 52(7): 1814-1829 (2004) - [c12]Sina Farsiu, Michael Elad, Peyman Milanfar:
Multiframe demosaicing and super-resolution from undersampled color images. Computational Imaging 2004: 222-233 - [c11]Andrew Segall, Michael Elad, Peyman Milanfar, Richard Webb, Chad Fogg:
Improved high-definition video by encoding at an intermediate resolution. VCIP 2004 - 2003
- [j11]Ron Kimmel, Michael Elad, Doron Shaked, Renato Keshet, Irwin Sobel:
A Variational Framework for Retinex. Int. J. Comput. Vis. 52(1): 7-23 (2003) - [j10]Michael Elad, Ron Kimmel, Doron Shaked, Renato Keshet:
Reduced complexity Retinex algorithm via the variational approach. J. Vis. Commun. Image Represent. 14(4): 369-388 (2003) - [j9]Alfred M. Bruckstein, Michael Elad, Ron Kimmel:
Down-scaling for better transform compression. IEEE Trans. Image Process. 12(9): 1132-1144 (2003) - [c10]Yaakov Tsaig, Michael Elad, Gene H. Golub, Peyman Milanfar:
Optimal framework for low bit-rate block coders. ICIP (2) 2003: 219-222 - [c9]Sina Farsiu, M. Dirk Robinson, Michael Elad, Peyman Milanfar:
Fast and robust super-resolution. ICIP (2) 2003: 291-294 - 2002
- [j8]Michael Elad, Yacov Hel-Or, Renato Keshet:
Rejection based classifier for face detection. Pattern Recognit. Lett. 23(12): 1459-1471 (2002) - [j7]Michael Elad:
On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 11(10): 1141-1151 (2002) - [j6]Michael Elad, Alfred M. Bruckstein:
A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans. Inf. Theory 48(9): 2558-2567 (2002) - [c8]Ron Kimmel, Michael Elad, Doron Shaked, Renato Keshet, Irwin Sobel:
Variational famework for Retinex. Human Vision and Electronic Imaging 2002: 409-418 - 2001
- [j5]Michael Elad, Yacov Hel-Or:
A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur. IEEE Trans. Image Process. 10(8): 1187-1193 (2001) - [c7]Michael Elad, Ayellet Tal, Sigal Ar:
Content Based Retrieval of VRML Objects - An Iterative and Interactive Approach. Eurographics Multimedia Workshop 2001: 107-118 - [c6]Michael Elad, Alfred M. Bruckstein:
On sparse signal representations. ICIP (1) 2001: 3-6 - [c5]Michael Elad, Yacov Hel-Or, Renato Keshet:
Pattern Detection Using a Maximal Rejection Classifier. IWVF 2001: 514-524 - [c4]Alfred M. Bruckstein, Michael Elad, Ron Kimmel:
Down-Scaling for Better Transform Compression. Scale-Space 2001: 123-136
1990 – 1999
- 1999
- [j4]Michael Elad, Arie Feuer:
Super-Resolution Reconstruction of Image Sequences. IEEE Trans. Pattern Anal. Mach. Intell. 21(9): 817-834 (1999) - [j3]Michael Elad, Arie Feuer:
Superresolution restoration of an image sequence: adaptive filtering approach. IEEE Trans. Image Process. 8(3): 387-395 (1999) - [c3]Michael Elad, Patrick C. Teo, Yacov Hel-Or:
Optimal Filters for Gradient-based Motion Estimation. ICCV 1999: 559-565 - [c2]Michael Elad, Arie Feuer:
Super-Resolution Reconstruction of Continuous Image Sequences. ICIP (3) 1999: 459-463 - 1998
- [j2]Michael Elad, Arie Feuer:
Recursive Optical Flow Estimation - Adaptive Filtering Approach. J. Vis. Commun. Image Represent. 9(2): 119-138 (1998) - [c1]Tamir Sagi, Arie Feuer, Michael Elad:
The periodic step gradient descent algorithm - General analysis and application to the super resolution reconstruction problem. EUSIPCO 1998: 1-4 - 1997
- [j1]Michael Elad, Arie Feuer:
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Trans. Image Process. 6(12): 1646-1658 (1997)
Coauthor Index
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