Performance Analysis of Low-Flux Least-Squares Single-Pixel Imaging

Shin, Dongeek; Shapiro, Jeffrey H.; Goyal, Vivek K

doi:10.1109/lsp.2016.2617329

Cited by 17 publications

(8 citation statements)

References 27 publications

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“…Advances in computational ghost imaging led to the use of a spatial light modulator (SLM) to generate the random patterns 3 , 4 . However, the non-orthogonality of random patterns often means that more than N measurements are required for a high quality reconstruction of an N pixel image 16 . Improvements can be made by sampling a scene with patterns forming an orthogonal basis set, allowing, in principle, a perfect reconstruction of an N pixel image with N measurements 17 , 18 .…”

Section: Introductionmentioning

confidence: 99%

A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging

Sun

Meng

Edgar

et al. 2017

Sci Rep

231

154

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Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique requires sequential measurements resulting in a trade-off between spatial resolution and acquisition time, limiting real-time video applications to relatively low resolutions. Compressed sensing techniques can be used to improve this trade-off. However, in this low resolution regime, conventional compressed sensing techniques have limited impact due to lack of sparsity in the datasets. Here we present an alternative compressed sensing method in which we optimize the measurement order of the Hadamard basis, such that at discretized increments we obtain complete sampling for different spatial resolutions. In addition, this method uses deterministic acquisition, rather than the randomized sampling used in conventional compressed sensing. This so-called ‘Russian Dolls’ ordering also benefits from minimal computational overhead for image reconstruction. We find that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction. Therefore, the proposed method may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition.

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Section: Introductionmentioning

confidence: 99%

A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging

Sun

Meng

Edgar

et al. 2017

Sci Rep

231

154

View full text Add to dashboard Cite

show abstract

“…In this method, T is multiplied both the sides Eq. (1) and then X is calculated which is equivalent to minimizing l 2 ball when m ≥ N [45]. However, its application is limited in SPI since it won't work for m < N , i.e.…”

Section: ) Comparison Investigation With Other Methodsmentioning

confidence: 99%

Active Mode Single Pixel Imaging in the Highly Turbid Water Environment Using Compressive Sensing

et al. 2019

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Underwater imaging has always been a challenge due to limitations imposed by scattering and absorption nature of the underwater environment. The light would be highly degraded after reflection and propagation in the water medium. Being an advanced imaging technique, Single-pixel Imaging (SPI) is applicable to acquire object spatial information in low light, severe backscattering, and high absorption conditions. Combination of Compressive Sensing (CS) and SPI can overcome the limitation of SPI algorithms such as long data-acquisition time, low reconstruction efficiency and poor reconstruction quality. In the current research, an underwater SPI system based on CS is established to reconstruct our two-dimensional (2D) transparent object. We have systematically investigated the influence of water turbid degree, measurement pattern types and number of measurements on image reconstruction performance. The proposed system is capable to reconstruct the object even when the turbidity reaches up to 80 Nephelometric Turbidity Unit (NTU), where the conventional imaging systems are unusable. Proposed reconstruction method in our research can save more than 70% data acquisition time, compared to SPI algorithm. Our experimental setup has been compared to a conventional imaging system and an underwater ghost imaging system to show its efficiency in obtaining accurate results from turbid water conditions. Furthermore, various algorithm comparison and imaging enhancement studies demonstrates that our algorithm is superior in bringing highly convex optimization at a faster rate with a smaller number of measurements. This work creates new insight into the SPI application and generates a guideline for researchers to improve their applications.INDEX TERMS Single pixel imaging, compressive sensing, gated techniques, imaging through turbid media.

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“…In its quantum version, ghost imaging refers to the use of parametric downconversion to create pairs of photons with correlated positions. If we detect the position of one photon with a standard camera and illuminate an object with the other position-correlated photon, it is sufficient to detect only the reflectance or transmittance of the object with a single-pixel detector-i.e., to measure the correlation count between the beams to then reconstruct a full image by repeating the measurement with many different photon pairs (each of which will be randomly distributed because of the random nature of the correlated photon pair generation process) (5,6). It is now acknowledged that the quantum properties of the correlated photons play no role in the image reconstruction process: Thermal light split into two beams using a beam splitter can be used equally effectively, albeit at a higher photon flux (7).…”

Section: Single-pixel and Ghost Imagingmentioning

confidence: 99%

Quantum-inspired computational imaging

Altmann

McLaughlin

Padgett

et al. 2018

Science

Self Cite

159

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Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.

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Performance Analysis of Low-Flux Least-Squares Single-Pixel Imaging

Cited by 17 publications

References 27 publications

A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging

A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging

Active Mode Single Pixel Imaging in the Highly Turbid Water Environment Using Compressive Sensing

Quantum-inspired computational imaging

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