We consider the problem of recovering block-sparse signals whose cluster patterns are
unknown a priori. Block-sparse signals with nonzero coefficients occurring in clusters arise …

We consider the problem of recovering 2D block-sparse signals with unknown cluster
patterns. The 2D block-sparse patterns arise naturally in many practical applications, such …

Image deconvolution and reconstruction are inverse problems which are encountered in a
wide array of applications. Due to the ill-posedness of such problems, their resolution …

Abstract Machine learning algorithms relying on deep neural networks recently allowed a
great leap forward in artificial intelligence. Despite the popularity of their applications, the …

We consider the problem of recovering block sparse signals with unknown block partition
and propose a better alternative to the extended block sparse Bayesian learning (EBSBL) …

In this paper, we develop a novel structured Bayesian compressive sensing algorithm with
location dependence for high-resolution imaging in ultra-narrowband passive synthetic …

Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of
artifacts and noises originated from both external and internal sources. The presence of …

In this paper, we propose a robust tensor-based scheme for joint direction-of-departure
(DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output …

To encourage the continuity of the target scene, a novel sparse representation (SR)-based
inverse synthetic aperture radar (ISAR) imaging algorithm is proposed by leveraging the …

In this paper, we address the design of message-passing receiver for massive multiple-input
multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid …