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Instance Optimal Sparse Recovery from Nonlinear Observations: A Unified Framework
Junren Chen, Arian Maleki
This paper develops a unified framework for instance optimal sparse recovery from nonlinear observations. The main ingredient is a signal-dependent restricted approximate invertibi…
Phase Transitions in Phase-Only Compressed Sensing
Junren Chen, Lexiao Lai, Arian Maleki
The goal of phase-only compressed sensing is to recover a structured signal from the phases under some complex-valued sen…
Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise
Xi Chen, Zhewen Hou, Christopher A. Metzler +2
We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as lo…
Towards Designing Optimal Sensing Matrices for Generalized Linear Inverse Problems
Junjie Ma, Ji Xu, Arian Maleki
We consider an inverse problem , where is the signal of interest, is the sensing matrix, is a nonlinear fun…
Compressed sensing in the presence of speckle noise
Wenda Zhou, Shirin Jalali, Arian Maleki
The problem of recovering a structured signal from its linear measurements in the presence of speckle noise is studied. This problem appears in many imaging systems such as synthet…
Mismatched Data Detection in Massive MU-MIMO
Charles Jeon, Arian Maleki, Christoph Studer
We investigate mismatched data detection for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems in which the prior distribution of the transmit signal u…