Compressive sensing reconstruction algorithms using partially correct signal information
Compressive Sensing (CS) ensures the reconstruction of a sparse signal from a set of linear measurements that are fewer compared to the signal length. The sparse signal can be reconstructed using a convex relaxation algorithm such as Basis Pursuit (BP) or a Greedy Pursuit (GP) such as Backtrackin...
Saved in:
Main Author: | Sekar Sathiya Narayanan |
---|---|
Other Authors: | Anamitra Makur |
Format: | Theses and Dissertations |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/69572 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Asymptotic performance analysis of compressed sensing reconstruction algorithm
by: Ong, Yan Lin
Published: (2019) -
Compressive sensing based video object compression schemes for surveillance systems
by: Narayanan, Sathiya, et al.
Published: (2018) -
ISAR signal reconstruction using compressive sensing in MATLAB
by: Meiklejohn, Stewart
Published: (2016) -
Compressive sensing algorithms for recovery of sparse and low rank signals
by: Mukund Sriram Narasimhan
Published: (2021) -
Speech enhancement using auditory-based spectral amplitude estimators
by: Sekar Sathiya Narayanan.
Published: (2013)