Imaging spectroscopy involves the sensing of a large amount of spatial information across a
multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent …

Techniques from sparse signal representation are beginning to see significant impact in
computer vision, often on nontraditional applications where the goal is not just to obtain a …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

Most compressive sensing (CS) reconstruction methods can be divided into two categories,
ie model-based methods and classical deep network methods. By unfolding the iterative …

Sparse representation models code an image patch as a linear combination of a few atoms
chosen out from an over-complete dictionary, and they have shown promising results in …

As a recently proposed technique, sparse representation based classification (SRC) has
been widely used for face recognition (FR). SRC first codes a testing sample as a sparse …

Abstract Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction
information of several electromagnetic waves/sources from the outputs of a number of …

A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in
this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can …

As a powerful statistical image modeling technique, sparse representation has been
successfully used in various image restoration applications. The success of sparse …