Description

The problem of sparse signal recovery has received much attention with the development of compressed sensing and the results providing insights into a wide-spread range of fields including signal processing, applied mathematics, statistics, computer science and more. The key idea behind sparse signal recovery is that any high-dimensional sparse signal can be successfully recovered from its significantly fewer suitable linear observations. In the past decade, this problem has been largely investigated in both theory and algorithm aspects, and has also bear fruitful applications including data compression, dictionary learning, image and video processing, machine learning and high-dimensional statistical inference. Recently, the research focus of this problem has been largely extended to deal with several new and different sparse recovery tasks, such as the sparse signal recovery corrupted with the non-Gaussian noise, neural network based methods for sparse signal recovery and the recovery of low-rank matrix and tensor recovery, to name a few.

The goal of this special section is to gather the current state-of-the-art advances in theory, algorithms and applications of the sparse recovery of signals, low-rank matrices and low-rank tensors, with the goals to highlight new achievements and developments and promising new directions and extensions. Both survey papers and the papers of original contributions that enhance the existing body of sparse signal recovery are also highly encouraged.

---

Lead Guest Editor

• Jianjun Wang, Southwest University, China

Guest Editors

• Jinming Wen, Jinan University, China
• Jiankang Zhang, University of Southampton, UK
• Wengu Chen, Beijing Institute of Applied Physics and Computational Mathematics, China
• Lisimachos P. Kondi, University of Ioannina, Greece
• Arun Kumar Sangaiah, VIT University, Vellore, India
• Eva Lagunas, University of Luxembourg, Luxembourg

Follow @eurasip