54 citations · 62 across the 5 of their papers we have counts for
5 papers
Compressed Learning: A Deep Neural Network Approach
Amir Adler, Michael Elad, Michael Zibulevsky
Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projection…
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Elad Richardson, Rom Herskovitz, Boris Ginsburg +1
We present SEBOOST, a technique for boosting the performance of existing stochastic optimization methods. SEBOOST applies a secondary optimization process in the subspace spanned b…
Designing and using prior knowledge for phase retrieval
Eliyahu Osherovich, Michael Zibulevsky, Irad Yavneh
In this work we develop an algorithm for signal reconstruction from the magnitude of its Fourier transform in a situation where some (non-zero) parts of the sought signal are known…
Phase retrieval combined with digital holography
Eliyahu Osherovich, Michael Zibulevsky, Irad Yavneh
We present a new method for real- and complex-valued image reconstruction from two intensity measurements made in the Fourier plane: the Fourier magnitude of the unknown image, and…
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning
Joseph Shtok, Michael Zibulevsky, Michael Elad
We propose a direct reconstruction algorithm for Computed Tomography, based on a local fusion of a few preliminary image estimates by means of a non-linear fusion rule. One such ru…