Enhanced Signal Recovery via Sparsity Inducing Image Priors

Parsimony in signal representation is a topic of active research. Sparse signal processing and representation is the outcome of this line of research which has many applications in information processing and has shown significant improvement in real-world applications such as recovery, classificatio...

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Bibliographic Details
Published in:arXiv.org 2018-05
Main Author: Hojjat Seyed Mousavi
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Data processing
Image enhancement
Recovery
Representations
Signal processing
Signal reconstruction
Sparsity
Online Access:Get full text
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Summary:Parsimony in signal representation is a topic of active research. Sparse signal processing and representation is the outcome of this line of research which has many applications in information processing and has shown significant improvement in real-world applications such as recovery, classification, clustering, super resolution, etc. This vast influence of sparse signal processing in real-world problems raises a significant need in developing novel sparse signal representation algorithms to obtain more robust systems. In such algorithms, a few open challenges remain in (a) efficiently posing sparsity on signals that can capture the structure of underlying signal and (b) the design of tractable algorithms that can recover signals under aforementioned sparse models.
ISSN:2331-8422