Compressed sensing in the far-field of the spatial light modulator in high noise conditions

AbstractSingle-pixel imaging techniques as an alternative to focal-plane detector arrays are being widely investigated. The interest in these single-pixel techniques is partly their compatibility with compressed sensing but also their applicability to spectral regions where focal planes arrays are simply not obtainable. Here, we show how a phased-array modulator source can be used to create Hadamard intensity patterns in the far-field, thereby enabling single-pixel imaging. Further, we successfully illustrate an implementation of compressed sensing for image reconstruction in conditions of high noise. In combination, this robust technique could be applied to any spectral region where spatial light phase modulators or phased-array sources are available.

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Compressed sensing in the far-field of the spatial light modulator in high noise conditions

10.21203/rs.3.rs-403263/v1 ◽

2021 ◽

Author(s):

Akhil Kallepalli ◽

John Innes ◽

Miles Padgett

Keyword(s):

Compressed Sensing ◽

Phased Array ◽

Imaging Techniques ◽

Far Field ◽

Light Modulator ◽

High Noise ◽

Detector Arrays ◽

Phase Modulators ◽

Plane Detector ◽

Single Pixel

Abstract Single-pixel imaging techniques as an alternative to focal-plane detector arrays are being widely investigated. The interest in these single-pixel techniques is partly their compatibility with compressed sensing but also their applicability to spectral regions where focal planes arrays are simply not obtainable. Here, we show how a phased-array modulator source can be used to create Hadamard intensity patterns in the far-field, thereby enabling single-pixel imaging. Further, we successfully illustrate an implementation of compressed sensing for image reconstruction in conditions of high noise. In combination, this robust technique could be applied to any spectral region where spatial light phase modulators or phased-array sources are available.

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Spatial Terahertz-Light Modulators for Single-Pixel Cameras

10.5772/intechopen.96691 ◽

2021 ◽

Author(s):

Rayko Ivanov Stantchev ◽

Emma Pickwell-MacPherson

Keyword(s):

Medical Diagnosis ◽

Imaging Techniques ◽

Terahertz Imaging ◽

Main Section ◽

Light Modulator ◽

Future Directions ◽

Imaging Arrays ◽

Light Modulators ◽

The Future ◽

Single Pixel

Terahertz imaging looks set to become an integral part of future applications from semiconductor quality control to medical diagnosis. This will only become a reality when the technology is sufficiently cheap and capabilities adequate to compete with others. Single-pixel cameras use a spatial light modulator and a detector with no spatial-resolution in their imaging process. The spatial-modulator is key as it imparts a series of encoding masks on the beam and the detector measures the dot product of each mask and the object, thereby allowing computers to recover an image via post-processing. They are inherently slower than parallel-pixel imaging arrays although they are more robust and cheaper, hence are highly applicable to the terahertz regime. This chapter dedicates itself to terahertz single-pixel cameras; their current implementations, future directions and how they compare to other terahertz imaging techniques. We start by outlining the competing imaging techniques, then we discuss the theory behind single-pixel imaging; the main section shows the methods of spatially modulating a terahertz beam; and finally there is a discussion about the future limits of such cameras and the concluding remarks express the authors’ vision for the future of single-pixel THz cameras.

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Digital Predistortion of Phased-Array Transmitter With Shared Feedback and Far-Field Calibration

IEEE Transactions on Microwave Theory and Techniques ◽

10.1109/tmtt.2020.3038193 ◽

2021 ◽

Vol 69 (1) ◽

pp. 1000-1015

Author(s):

Nuutti Tervo ◽

Bilal Khan ◽

Olli Kursu ◽

Janne P. Aikio ◽

Markku Jokinen ◽

...

Keyword(s):

Phased Array ◽

Far Field ◽

Digital Predistortion ◽

Field Calibration

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Comparison and Application of Near-Field ISAR Imaging Techniques for Far-Field Radar Cross Section Determination

IEEE Transactions on Antennas and Propagation ◽

10.1109/tap.2005.861549 ◽

2006 ◽

Vol 54 (1) ◽

pp. 144-151 ◽

Cited By ~ 59

Author(s):

T. Vaupel ◽

T.F. Eibert

Keyword(s):

Cross Section ◽

Imaging Techniques ◽

Near Field ◽

Radar Cross Section ◽

Far Field ◽

Isar Imaging

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SYNTHETICALLY FOCUSED IMAGING TECHNIQUES IN SIMULATED AUSTENITIC STEEL WELDS USING AN ULTRASONIC PHASED ARRAY

10.1063/1.3362511 ◽

2010 ◽

Cited By ~ 1

Author(s):

G. D. Connolly ◽

M. J. S. Lowe ◽

S. I. Rokhlin ◽

J. A. G. Temple ◽

Donald O. Thompson ◽

...

Keyword(s):

Austenitic Steel ◽

Phased Array ◽

Imaging Techniques ◽

Ultrasonic Phased Array ◽

Steel Welds

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A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation

Journal of Applied Mathematics ◽

10.1155/2013/548979 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Jing Liu ◽

ChongZhao Han ◽

XiangHua Yao ◽

Feng Lian

Keyword(s):

Compressed Sensing ◽

Phased Array ◽

Reduction Method ◽

Doa Estimation ◽

Estimation Methods ◽

Sensing Matrix ◽

Replacement Method ◽

Precise Estimation ◽

Novel Method ◽

Measurement Vector

A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. The measurement vector is changed accordingly. It is proved that the original sparse signal could be reconstructed well from the newly changed measurement vector based on the new sensing matrix with large probability. This method is then extended to a more practical condition when highly coherent columns and incoherent columns are considered, for example, the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, where the compressed sensing method based on the original sensing matrix fails. The proposed method also obtains more precise estimation of DOA using one snapshot compared with the traditional estimation methods such as Capon, APES, and GLRT, based on hundreds of snapshots.

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Optical phased array for far field beam steering with varied HCG

10.1117/12.909737 ◽

2012 ◽

Cited By ~ 1

Author(s):

Weiwei Hu ◽

Connie Chang-Hasnain

Keyword(s):

Phased Array ◽

Beam Steering ◽

Far Field ◽

Optical Phased Array

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Far-field super-resolution ghost imaging with a deep neural network constraint

Light Science & Applications ◽

10.1038/s41377-021-00680-w ◽

2022 ◽

Vol 11 (1) ◽

Author(s):

Fei Wang ◽

Chenglong Wang ◽

Mingliang Chen ◽

Wenlin Gong ◽

Yu Zhang ◽

...

Keyword(s):

Neural Network ◽

Physical Model ◽

Deep Neural Network ◽

Super Resolution ◽

Far Field ◽

Ghost Imaging ◽

Practical Applications ◽

Sampling Ratio ◽

Network Output ◽

Single Pixel

AbstractGhost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.

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