activity
20092026
most citedThis is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms - Theory and Practice

289 citations · 1.2k across the 72 of their papers we have counts for

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Showing cs.ITShow all

6 papers · 1 filter

cs.IT2013★ 6 cited

Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction

Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett

This paper describes a coded aperture and keyed exposure approach to compressive video measurement which admits a small physical platform, high photon efficiency, high temporal res…

cs.IT2010★ 55 cited

Performance bounds for expander-based compressed sensing in Poisson noise

Maxim Raginsky, Sina Jafarpour, Zachary Harmany +3

This paper provides performance bounds for compressed sensing in the presence of Poisson noise using expander graphs. The Poisson noise model is appropriate for a variety of applic…

cs.IT2010

Fishing in Poisson streams: focusing on the whales, ignoring the minnows

Maxim Raginsky, Sina Jafarpour, Rebecca Willett +1

This paper describes a low-complexity approach for reconstructing average packet arrival rates and instantaneous packet counts at a router in a communication network, where the arr…

cs.IT2009★ 3 cited

Performance Bounds for Expander-based Compressed Sensing in the presence of Poisson Noise

Sina Jafarpour, Rebecca Willett, Maxim Raginsky +1

This paper provides performance bounds for compressed sensing in the presence of Poisson noise using expander graphs. The Poisson noise model is appropriate for a variety of applic…

cs.IT2009★ 164 cited

Compressed sensing performance bounds under Poisson noise

Maxim Raginsky, Rebecca M. Willett, Zachary T. Harmany +1

This paper describes performance bounds for compressed sensing (CS) where the underlying sparse or compressible (sparsely approximable) signal is a vector of nonnegative intensitie…

cs.IT2009

Performance bounds on compressed sensing with Poisson noise

Rebecca M. Willett, Maxim Raginsky

This paper describes performance bounds for compressed sensing in the presence of Poisson noise when the underlying signal, a vector of Poisson intensities, is sparse or compressib…