Blind image deblurring (BID) has been extensively studied in computer vision and adjacent
fields. Modern methods for BID can be grouped into two categories: single-instance methods …

Blind deconvolution (BD), the resolution of a signal and a filter given their convolution, arises
in many applications. Without further constraints, BD is ill-posed. In practice, subspace or …

Subsampled blind deconvolution is the recovery of two unknown signals from samples of
their convolution. To overcome the ill-posedness of this problem, solutions based on priors …

We study identifiability for bilinear inverse problems under sparsity and subspace
constraints. We show that, up to a global scaling ambiguity, almost all such maps are …

Multi-channel sparse blind deconvolution, or convolutional sparse coding, refers to the
problem of learning an unknown filter by observing its circulant convolutions with multiple …

We study the convolutional phase retrieval problem, of recovering an unknown signal x∈ C
n from m measurements consisting of the magnitude of its cyclic convolution with a given …

Fast transforms correspond to factorizations of the form, where each factor is sparse and
possibly structured. This paper investigates essential uniqueness of such factorizations, ie …

We consider the multichannel blind deconvolution problem where we observe the output of
multiple channels that are all excited with the same unknown input. From these …

Tensor methods have emerged as a powerful paradigm for consistent learning of many
latent variable models such as topic models, independent component analysis and …

The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one
bilinear measurements. We consider a natural nonsmooth formulation of the problem and …