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Compressed Sensing: Mathematical Foundations, Implementation, and Advanced Optimization Techniques

2025/09/15 by Stevenson, Shane, Maryam Sabagh, Sabagh, Maryam
Computer Science · Engineering · #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.2509.11550

openalex publication_date 2025/09/15 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28

Abstract

Compressed sensing is a signal processing technique that allows for the reconstruction of a signal from a small set of measurements. The key idea behind compressed sensing is that many real-world signals are inherently sparse, meaning that they can be efficiently represented in a different space with only a few components compared to their original space representation. In this paper we will explore the mathematical formulation behind compressed sensing, its logic and pathologies, and apply compressed sensing to real world signals.

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