Sparse representations of dynamic scenes for compressive spectral video sensing

The coded aperture snapshot spectral imager (CASSI) is an optical architecture that captures spectral images using compressive sensing. This system improves the sensing speed and reduces the large amount of collected data given by conventional spectral imaging systems. In several applications, it is necessary to analyze changes that occur between short periods of time. This paper first presents a sparsity analysis for spectral video signals, to obtain accurate approximations and better comply compressed sensing theory. The use of the CASSI system in compressive spectral video sensing then is proposed. The main goal of this approach is to capture the spatio-spectral information of dynamic scenes using a 2-dimensional set of projections. This application involves the use of a digital micro-mirror device that implements the traditional coded apertures used by CASSI. Simulations show that accurate reconstructions along the spatial, spectral and temporal axes are attained, with PSNR values of around 30 dB.

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GENERATION OF DYNAMIC SCENES FOR TESTING INFRARED IMAGING SYSTEMS IN THE FAR INFRARED RANGE

Автометрия ◽

10.15372/aut20180102 ◽

2018 ◽

Keyword(s):

Infrared Imaging ◽

Far Infrared ◽

Imaging Systems ◽

Infrared Range ◽

Dynamic Scenes

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Using orthogonal self-loop coded aperture in a pushbroom computational spectral imager

10.1117/12.2021596 ◽

2013 ◽

Author(s):

Yang-yang Liu ◽

Bin Xiangli ◽

Qun-bo Lv ◽

Huang Min ◽

Zhou Jinsong

Keyword(s):

Coded Aperture ◽

Spectral Imager

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Parallel Coded Aperture Method for Hyperspectral Compressive Sensing on GPU

Compressive Sensing of Earth Observations ◽

10.1201/9781315154626-14 ◽

2017 ◽

pp. 313-328

Author(s):

Gabriel Martín ◽

José Nascimento ◽

José Bioucas-Dias

Keyword(s):

Compressive Sensing ◽

Coded Aperture

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A coded aperture microscope for X-ray fluorescence full-field imaging

Journal of Synchrotron Radiation ◽

10.1107/s1600577520012308 ◽

2020 ◽

Vol 27 (6) ◽

pp. 1703-1706

Author(s):

D. P. Siddons ◽

A. J. Kuczewski ◽

A. K. Rumaiz ◽

R. Tappero ◽

M. Idir ◽

...

Keyword(s):

Silicon Nitride ◽

Near Field ◽

Coded Aperture ◽

Full Field ◽

X Ray ◽

Silicon Nitride Membrane ◽

Coded Apertures ◽

Redundant Arrays ◽

Field Imaging ◽

Good Contrast

The design and construction of an instrument for full-field imaging of the X-ray fluorescence emitted by a fully illuminated sample are presented. The aim is to produce an X-ray microscope with a few micrometers spatial resolution, which does not need to scan the sample. Since the fluorescence from a spatially inhomogeneous sample may contain many fluorescence lines, the optic which will provide the magnification of the emissions must be achromatic, i.e. its optical properties must be energy-independent. The only optics which fulfill this requirement in the X-ray regime are mirrors and pinholes. The throughput of a simple pinhole is very low, so the concept of coded apertures is an attractive extension which improves the throughput by having many pinholes, and retains the achromatic property. Modified uniformly redundant arrays (MURAs) with 10 µm openings and 50% open area have been fabricated using gold in a lithographic technique, fabricated on a 1 µm-thick silicon nitride membrane. The gold is 25 µm thick, offering good contrast up to 20 keV. The silicon nitride is transparent down into the soft X-ray region. MURAs with various orders, from 19 up to 73, as well as their respective negative (a mask where open and closed positions are inversed compared with the original mask), have been made. Having both signs of mask will reduce near-field artifacts and make it possible to correct for any lack of contrast.

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Comparative noise performance of a coded aperture spectral imager

10.1117/12.2240926 ◽

2016 ◽

Author(s):

Jonathan Piper ◽

Peter Yuen ◽

Peter Godfree ◽

Mengjia Ding ◽

Umair Soori ◽

...

Keyword(s):

Coded Aperture ◽

Noise Performance ◽

Spectral Imager

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Study on Over-Complete Dictionaries for Sparse Representations of Signals

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.796 ◽

2012 ◽

Vol 157-158 ◽

pp. 796-799

Author(s):

Guang Chun Gao ◽

Kai Xiong ◽

Li Na Shang ◽

Sheng Ying Zhao ◽

Cui Zhang

Keyword(s):

Inverse Problems ◽

Compressed Sensing ◽

Sparse Representation ◽

Sparse Representations ◽

Experimental Results ◽

Decomposition Algorithm ◽

Linear Combinations ◽

Design Algorithm ◽

Sparse Decomposition

In recent years there has been a growing interest in the study of sparse representation of signals. The redundancy of over-complete dictionary can make it effectively capture the characteristics of the signals. Using an over-complete dictionary that contains prototype signal-atoms, signals are described as linear combinations of a few of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, Compressed Sensing (CS), and more. Recent activities in this field concentrate mainly on the study of sparse decomposition algorithm and dictionary design algorithm. In this paper, we discuss the advantages of sparse dictionaries, and present the implicit dictionaries for signal sparse presents. The overcomplete dictionaries which combined the different orthonormal transform bases can be used for the compressed sensing. Experimental results demonstrate the effectivity for sparse presents of signals.

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GPU implementation of a hyperspectral coded aperture algorithm for compressive sensing

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2015.7325815 ◽

2015 ◽

Cited By ~ 2

Author(s):

Sergio Bernabe ◽

Gabriel Martin ◽

Jose M. P. Nascimento ◽

Jose M. Bioucas-Dias ◽

Antonio Plaza ◽

...

Keyword(s):

Compressive Sensing ◽

Coded Aperture ◽

Gpu Implementation

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Review of Sparsity Known and Blind Sparsity Greedy Algorithms for Compressed Sensing

Intelligent Image and Video Interpretation ◽

10.4018/978-1-4666-3958-4.ch004 ◽

2013 ◽

pp. 125-138

Author(s):

Chun-Yan Zeng ◽

Li-Hong Ma ◽

Ming-Hui Du ◽

Jing Tian

Keyword(s):

Compressed Sensing ◽

Compressive Sensing ◽

Matching Pursuit ◽

Greedy Algorithms ◽

Diagrammatic Representation ◽

Support Set ◽

Exact Reconstruction ◽

Signal Characteristics ◽

Sparsity Level

Sparsity level is crucial to Compressive Sensing (CS) reconstruction, but in practice it is often unknown. Recently, several blind sparsity greedy algorithms have emerged to recover signals by exploiting the underlying signal characteristics. Sparsity Adaptive Matching Pursuit (SAMP) estimates the sparsity level and the true support set stage by stage, while Backtracking-Based Adaptive OMP (BAOMP) selects atoms by thresholds related to the maximal residual projection. This chapter reviews typical sparsity known greedy algorithms including OMP, StOMP, and CoSaMP, as well as those emerging blind sparsity greedy algorithms. Furthermore, the algorithms are analysed in structured diagrammatic representation and compared by exact reconstruction probabilities for Gaussian and binary signals distributed sparsely.

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