Journal Research Projects

Blind Image Deconvolution Using Deep Generative Priors

This article proposes a novel approach to regularize the ill-posed and non-linear blind image deconvolution (blind deblurring) using deep generative networks as priors. We employ two separate pretrained generative networks — given lower-dimensional Gaussian vectors as input, one of the generative models samples from the distribution of sharp images, while the other from that of […]

Journal Research Projects

Compressive Sampling of Ensembles
of Correlated Signals

The recently developed theory of Compressive sensing (CS) has shown that sparse signals can be reconstructed from a much smaller number of measurements than their bandwidth suggests. In this paper we present a sampling scheme to acquire ensembles of correlated signals at a sub-Nyquist rate. The sampling architecture uses simple analog building blocks including analog […]

Journal Research Projects

Blind Deconvolution Using Modulated Inputs

This paper considers the blind deconvolution of multiple modulated signals/filters, and an arbitrary filter/signal. Multiple inputs are modulated (pointwise multiplied) with random sign sequences , respectively, and the resultant inputs are convolved against an arbitrary input to yield the measurements , where and denote pointwise multiplication, and circular convolution. Given , we want to recover […]

Journal Research Projects

Compressed Sensing based Robust Phase Retrieval
via Deep Generative Priors

Algorithmic phase retrieval offers an alternative means to recover the phase of optical images without requiring sophisticated measurement setups such as holography. This paper proposes a framework to regularize the highly ill-posed and nonlinear phase retrieval problem through deep generative priors by simply using gradient descent algorithm. We experimentally show effectiveness of the proposed approach […]

Journal Research Projects

Blind Deconvolution Using Convex Programming

We consider the problem of recovering two unknown vectors, and , of length from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension and the other with dimension . Although the observed convolution is nonlinear in both and , it is linear in the […]

Journal Research Projects

Compressive Acquisition and Least-Squares Reconstruction of Correlated Signals

This letter presents a framework for the compressive acquisition of correlated signals. We propose an implementable sampling architecture for the acquisition of ensembles of correlated (lying in an a priori unknown subspace) signals at a sub-Nyquist rate. The sampling architecture acquires structured compressive samples of the signals after preprocessing them with easy-toimplement components. Quantitatively, we […]

Journal Research Projects

A Convex Approach to Blind MIMO Communications

This letter considers the blind separation of convolutive mixtures in a multi-in-multi-out (MIMO) communication system. Multiple source signals are transmitted simultaneously over a shared communication medium (modeled as linear convolutive channels) to multiple receivers. We recast the joint recovery of the source signals, and the channel impulse responses as a block-rank-one matrix recovery problem, which […]

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