Enhanced Signal Recovery via Sparsity Inducing Image Priors

2018/05/13 by Hojjat Seyed Mousavi, Mousavi, Hojjat Seyed
Computer Science · Engineering · #Blind Source Separation Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1805.04828

openalex publication_date 2018/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing

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