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Universal Compressed Sensing [PDF]

2016 IEEE International Symposium on Information Theory (ISIT), 2016
In this paper, the problem of developing universal algorithms for compressed sensing of stochastic processes is studied. First, R\'enyi's notion of information dimension (ID) is generalized to analog stationary processes.
Jalali, Shirin, Poor, H. Vincent
core   +2 more sources

"Compressed" Compressed Sensing

, 2010
The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or samples).
Gastpar, Michael, Reeves, Galen
core   +2 more sources

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review [PDF]

Sensors
Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging.
Yuanmao Wang, Yang Chen, Yongjian Zhao, Siyu Liu   +3 more
doaj   +2 more sources

Single-Shot Compressed Imaging via Random Phase Modulation

Applied Sciences, 2022
Compressed sensing (CS) provides an innovative framework for signal sampling, which enables accurate recovery of the sparse or compressible signal from a small set of linear measurements far fewer than the Nyquist rate in traditional signal processing ...
Cheng Zhang, Ru Zhang, Yuanyuan Zhu, Hairong Yang, Chuan Shen, Sui Wei   +5 more
doaj   +1 more source

Distributed Compressed Sensing of Hyperspectral Images According to Spectral Library Matching

IEEE Access, 2021
The ever-increasing resolution puts tremendous pressure to the onboard hyperspectral imaging system. Compressed sensing technology is one of the important ways to solve this problem.
Hua Xiao, Zhongliang Wang, Xueying Cui
doaj   +1 more source

COMPRESSIVE SENSING

International Journal of Engineering Technologies and Management Research, 2020
Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality.
Karl-Dirk Kammeyer, Armin Dekorsy, Peter Klenner, Mark Petermann   +3 more
openaire   +3 more sources

Compressed wavefront sensing [PDF]

Optics Letters, 2014
We report on an algorithm for fast wavefront sensing that incorporates sparse representation for the first time in practice. The partial derivatives of optical wavefronts were sampled sparsely with a Shack-Hartman wavefront sensor (SHWFS) by randomly subsampling the original SHWFS data to as little as 5%.
James, Polans, Ryan P, McNabb, Joseph A, Izatt, Sina, Farsiu   +3 more
openaire   +2 more sources

Fully Learnable Model for Task-Driven Image Compressed Sensing

Sensors, 2021
This study primarily investigates image sensing at low sampling rates with convolutional neural networks (CNN) for specific applications. To improve the image acquisition efficiency in energy-limited systems, this study, inspired by compressed sensing ...
Bowen Zheng, Jianping Zhang, Guiling Sun, Xiangnan Ren   +3 more
doaj   +1 more source

An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher

Entropy, 2022
In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption.
Xiaomeng Song, Mengna Shi, Yanqi Zhou, Erfu Wang   +3 more
doaj   +1 more source

Quasi-linear Compressed Sensing [PDF]

Multiscale Modeling & Simulation, 2014
Inspired by significant real-life applications, in particular, sparse phase retrieval and sparse pulsation frequency detection in Asteroseismology, we investigate a general framework for compressed sensing, where the measurements are quasi-linear.
Martin Ehler, Massimo Fornasier, Juliane Sigl   +2 more
openaire   +2 more sources