Elad, Michael.
![Sudoc [ABES], France](/viaf/images/flags/SUDOC.png "Sudoc [ABES], France")
Michael Elad Israeli computer scientist
Elad, M. (Michael)
Elad, M.
אלעד, מיכאל
Elad, Michael, 1963-
VIAF ID: 199101132 (Personal)
Permalink: http://viaf.org/viaf/199101132
Preferred Forms
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100 1 _
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Elad, M.
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100 1 _
‡a
Elad, M.
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(Michael)
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100 1 _ ‡a Elad, M. ‡q (Michael)
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100 1 _ ‡a Elad, M. ‡q (Michael)
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100 1 _ ‡a Elad, Michael
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100 1 _ ‡a Elad, Michael
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100 1 _ ‡a Elad, Michael
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100 1 _
‡a
Elad, Michael
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100 1 _
‡a
Elad, Michael
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100 1 _ ‡a Elad, Michael, ‡d 1963-
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100 1 _ ‡a Elad, Michael.
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100 0 _
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Michael Elad
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Israeli computer scientist
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100 1 _ ‡a אלעד, מיכאל
4xx's: Alternate Name Forms (12)
Works
| Title | Sources |
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| Adaptive filtered-back-projection for computed tomography |
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| Advances and Challenges in Super-Resolution |
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| Advances in Image Denoising. |
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| Analysis versus synthesis in signal priors |
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| Con-Patch: When a Patch Meets Its Context |
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| The Dantzig-Selector Algorithm |
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| Direct adaptive algorithms for CT reconstruction |
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| Epilogue |
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| Example-Based Image Synthesis via Randomized Patch-Matching. |
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| From Exact to Approximate Solutions |
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| Generalized Tree-Based Wavelet Transform |
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| Generalizing the nonlocal-means to super-resolution reconstruction. |
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| Generative modeling for weak lensing inverse problems |
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| Image and Graphics : 11th International Conference, ICIG 2021, Haikou, China, August 6-8, 2021, Proceedings, Part III |
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| Image Compression – Facial Images |
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| Image Deblurring – A Case Study |
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| Iterative-Shrinkage Algorithms |
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| Iterative signal recovery from incomplete samples |
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| Large Inpainting of Face Images With Trainlets |
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| MAP versus MMSE Estimation |
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| Modèles génératifs pour problèmes inverses de lentille gravitationnelle faible. |
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| Multiframe demosaicing and super-resolution of color images |
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| n2008048989 |
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| Optimized Pre-Compensating Compression |
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| Other Applications |
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| Prologue |
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| Pursuit Algorithms – Practice |
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| Quelque progrès en débruitage d'images |
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| The Quest for a Dictionary |
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| Restoration by Compression |
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| Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images |
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| RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT |
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| Robust shift and add approach to superresolution |
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| Single image interpolation via adaptive nonlocal sparsity-based modeling. |
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| Sparse and redundant representations : from theory to applications in signal and image processing |
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| Sparse representation for color image restoration |
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| Sparsity-Seeking Methods in Signal Processing |
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| Style Transfer Via Texture Synthesis |
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| Supasu moderingu : Eruwan eruzero norumu saishoka no kiso riron to gazo shori eno oyo. |
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| Super-resolution reconstruction of continuous image sequence, 1996: |
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| Super-resolution without explicit subpixel motion estimation |
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| Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks |
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| Towards Average PerformanceAnalysis |
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| Unified Single-Image and Video Super-Resolution via Denoising Algorithms |
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| Uniqueness and Uncertainty |
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| Unsupervised Single Image Dehazing Using Dark Channel Prior Loss |
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| A Variational Framework for Retinex |
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| Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences |
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| Why Simple Shrinkage Is Still Relevant for Redundant Representations? |
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| スパースモデリング : l1/l0ノルム最小化の基礎理論と画像処理への応用 |
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