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  1. Courses
  2. 2017 SIAM Annual Meeting
  3. Boosting and Learning in Ma...

Boosting and Learning in Mathematical Imaging Algorithms - Part I of II

AN17 - MS67-1: How to Improve Your Denoising Result Without Changing Your Denoising Algorithm

Presentation: Marcelo Bertalmío, Universitat Pompeu Fabra, Spain, 26 min 16 sec

AN17 - MS67-1: How to Improve Your Denoising Result Without Changing Your Denoising Algorithm

Document: AN17 - MS67-1: How to Improve Your Denoising Result Without Changing Your Denoising Algorithm(PDF)

AN17 - MS67-2: RED: Regularization by Denoising: The Little Engine that Could

Presentation: Yaniv Romano, Technion Israel Institute of Technology, Israel, 22 min 2 sec

AN17 - MS67-2: RED: Regularization by Denoising: The Little Engine that Could

Document: AN17 - MS67-2: RED: Regularization by Denoising: The Little Engine that Could(PDF)

AN17 - MS67-4: Not Afraid of the Dark: NIR-VIS Face Recognition Via Cross-spectral Hallucination and Low-Rank Embedding

Presentation: Qiang Qiu, Duke University, USA, 19 min 14 sec

AN17 - MS67-4: Not Afraid of the Dark: NIR-VIS Face Recognition Via Cross-spectral Hallucination and Low-Rank Embedding

Document: AN17 - MS67-4: Not Afraid of the Dark: NIR-VIS Face Recognition Via Cross-spectral Hallucination and Low-Rank Embedding(PDF)
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