Special Session 10  会议特别专题 10

Super-Resolution Theory, Methods, and Applications

Description: This special session aims to bring together recent advances in super-resolution for signal processing, focusing on both theoretical developments and practical applications. Super-resolution techniques, such as atomic norm minimization, gridless sparse signal processing, and low-rank matrix recovery, have shown great promise in resolving fine-grained structures beyond classical resolution limits. These methods have been widely adopted in line spectral estimation, direction-of-arrival (DOA) estimation, range-angle-Doppler imaging, and various other inverse problems across radar, array processing, and wireless communications. We welcome contributions that advance the theoretical understanding, algorithmic development, and domain-specific applications of super-resolution techniques.

Session organizers
Assoc. Prof. Feng Xi, Nanjing University of Science and Technology, China
Asst. Prof. Dehui Yang, Xi’an Jiaotong-Liverpool University, China
Assoc. Prof. Xiaohuan Wu, Nanjing University of Posts and Telecommunications, China

The topics of interest include, but are not limited to:
▪ Line spectral estimation and frequency recovery
▪ Atomic norm minimization and its performance analysis
▪ Gridless sparse signal processing and compressed sensing
▪ Super-resolution for array signal processing and DOA estimation
▪ Learning-aided super-resolution approaches for signal and image processing
▪ Super-resolution radar imaging and related inverse problems
▪ High-resolution parameter estimation techniques for wireless communications
▪ Convex and non-convex optimization techniques for super-resolution
▪ Low-rank matrix recovery and structured matrix completion algorithms
▪ Applications to MIMO radar, XL-MIMO, mmWave, and RIS-assisted systems

Submission method
Submit your Full Paper (no less than 4 pages with two colums) or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 10 (Super-Resolution Theory, Methods, and Applications)
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Introduction of Session organizers

Assoc. Prof. Feng Xi, Nanjing University of Science and Technology, China

Feng Xi received the Ph.D. degree in information and communication engineering from Nanjing University of Science and Technology, Nanjing, China. He is currently an Associate Professor in the Department of Electrical Engineering, Nanjing University of Science and Technology. From 2018 to 2019, he was a visiting scholar in the Preston M. Green Department of Electrical and Systems Engineering (ESE) at Washington University in St. Louis (WUSTL). His research interests include radar signal processing, statistical signal and array signal processing, compressive sensing, and convex optimization.



Asst. Prof. Dehui Yang, Xi’an Jiaotong-Liverpool University, China

Dehui Yang earned his PhD in Electrical Engineering from Colorado School of Mines in 2018. Before joining Xi’an Jiaotong-Liverpool University as an Assistant Professor in 2023, he worked as an applied scientist at Uber Technologies Inc from 2021 to 2023. Prior to that, he was one of the founding data scientists at Root Inc. His interests lie in the general areas of data science, including sparse signal processing and compressive sensing, and predictive modeling using statistical machine learning techniques. He serves as a reviewer for international journals, including IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Information Theory, IEEE Signal Processing Letters, IEEE Journal of Selected Topics in Signal Processing, Applied and Computational Harmonic Analysis, and Signal Processing.



Assoc. Prof. Xiaohuan Wu, Nanjing University of Posts and Telecommunications, China

Xiaohuan Wu received the B.S. and Ph.D. degrees from the College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China, in 2010 and 2017, respectively, where he is currently an Associate Professor. His current research interests include array signal processing, compressive sensing, millimeter communications and deep learning.