Compressive sensing algorithms for recovery of sparse and low rank signals
Many natural signals are atomic, i.e., the signals may be represented in some low dimensional space due to their inherent structure. Two most common atomic structures are sparsity and low rank. A sparse signal (vector/matrix) has very few nonzero entries. A low rank matrix has very small rank in c...
Saved in:
Main Author: | Mukund Sriram Narasimhan |
---|---|
Other Authors: | Anamitra Makur |
Format: | Thesis-Master by Research |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/145980 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Learning nonlocal sparse and low-rank models for image compressive sensing: nonlocal sparse and low-rank modeling
by: Zha, Zhiyuan, et al.
Published: (2023) -
Sparse signal processing and compressed sensing recovery
by: Sujit Kumar Sahoo
Published: (2014) -
Algorithms for recovery of sparse signals in compressed sensing
by: Tran, Anh Vu.
Published: (2013) -
Structured sparse signal recovery algorithms and their applications
by: Wang, Lu
Published: (2014) -
Sparse low-rank matrix approximation for data compression
by: Hou, Junhui, et al.
Published: (2018)