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Irad Yavneh
Researcher at Technion – Israel Institute of Technology
Publications - 126
Citations - 3409
Irad Yavneh is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Multigrid method & Sparse approximation. The author has an hindex of 31, co-authored 124 publications receiving 3185 citations. Previous affiliations of Irad Yavneh include National Center for Atmospheric Research & Weizmann Institute of Science.
Papers
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Journal ArticleDOI
Adaptive Multiscale Redistribution for Vector Quantization
Yair Koren,Irad Yavneh +1 more
TL;DR: This work presents a novel multiscale iterative scheme based on redistributing the representation levels among aggregates of decision regions at changing scales based on the so-called point density function and on the number of representation levels in each aggregate.
Proceedings ArticleDOI
Sparsity-Based Single-Shot Sub-Wavelength Coherent Diffractive Imaging
Yoav Shechtman,Alexander Szameit,Eliyahu Osherovich,Elad Bullkich,Hod Dana,Snir Gazit,Shy Shoham,Michael Zibulevsky,Irad Yavneh,E.-B. Kley,Yonina C. Eldar,Oren Cohen,Mordechai Segev +12 more
TL;DR: In this paper, the authors demonstrate theoretically and experimentally a method of performing single-shot sub-wavelength Coherent Diffractive Imaging (CDI), an algorithmic approach for reconstructing sparse subwavelength images from their far-field intensity measurements.
Journal ArticleDOI
Approximate Fourier phase information in the phase retrieval problem: what it gives and how to use it.
TL;DR: The main discovery is that a rough phase estimate (up to π/2 rad) allows development of very efficient algorithms whose reconstruction time is an order of magnitude faster than that of the current method of choice--the hybrid input-output (HIO) algorithm.
A Multigrid Approach for Fast Geodesic Active Contours
TL;DR: An implicit formulation of the geodesic active contour is proposed, which reduces the required number of timesteps drastically and an e‐cient adaptive multigrid algorithm is developed and implemented for the solution of the resulting nonlinear system.
Journal ArticleDOI
Accuracy Measures and Fourier Analysis for the Full Multigrid Algorithm
TL;DR: The full multigrid (FMG) algorithm is often claimed to achieve so-called discretization-level accuracy, but this notion is formalized by defining a worst-case relative accuracy measure, denoted $E_{FMG}^{\ell}$, which compares the total error of the $\ell$-level FMG solution against the inherent discretized error.