Hadamard transform-based hyperspectral imaging using a single-pixel detector

Qi, Yi; Heng, Lim Zi; Li, Liang; Zhou, Guangcan; Siong, Chau Fook; Zhou, Guangyong

doi:10.1364/oe.390490

Cited by 25 publications

(7 citation statements)

References 28 publications

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“…The image can be retrieved after correlating the speckle patterns with the corresponding light intensity values via a reconstruction algorithm. Benefiting from the low manufacturing cost, wide detection band, and highly sensitive responsiveness of single-pixel detectors, CGI is widely applied in various fields: color imaging [2][3][4], infrared imaging [5][6][7], terahertz imaging [8][9][10], spectral imaging [11][12][13], three-dimensional imaging [14][15][16], and biomedical imaging [17][18][19], etc. However, these works mostly deal with transmissive objects or reflective objects with Lambertian surfaces and rarely target scenarios containing specular surfaces.…”

Section: Introductionmentioning

confidence: 99%

Computational ghost imaging with adaptive intensity illumination for scenes featuring specular surfaces

Xiong,

Zhang,

et al. 2024

J. Opt.

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Imaging a target scene with specular surfaces is a daunting challenge for both direct imaging and indirect computational imaging techniques. The intense specular reflection component during the measurement severely degrades the quality of the reconstructed image, resulting in a substantial loss of scene information. To address this issue, we propose a computational ghost imaging （CGI） method with adaptive intensity illumination. Capitalizing on the encoded imaging feature of CGI, this method enables effective imaging of target scenes with specular surfaces through two series of measurements, eliminating the necessity for additional optical components. Based on the position and intensity information of pixels in the specular regions from the first series of measurements, our method modulates the illumination patterns to weaken the intensity of the specular region in the second series of measurements. Simulation and experimental results demonstrate that the utilization of these modulated illumination patterns for target scene measurement effectively mitigates interference from the specular surface during imaging. Consequently, the reconstructed image is capable of presenting more detailed information about the target scene other than the specular regions. Our work introduces a novel approach for imaging target scenes with specular surfaces and broadens the scope of applications for CGI in reality.

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Section: Introductionmentioning

confidence: 99%

Computational ghost imaging with adaptive intensity illumination for scenes featuring specular surfaces

Xiong,

Zhang,

et al. 2024

J. Opt.

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“…However, the need for high-resolution images in HSI to capture fine scene details inevitably leads to substantial data acquisition, increased sampling time (rate), and higher processing and storage costs. The introduction and fusion of compressed sensing (CS) algorithms and SPI provide one of the promising alternative solutions for HSI [26][27][28][29][30][31][32][33], effectively addressing the challenges associated with HSI by utilizing undersampling and single-pixel detection. An illustrative study demonstrates the effectiveness of a CS-based single-pixel HSI method for detecting the chemical composition of targets in the nearinfrared spectrum.…”

Section: Introductionmentioning

confidence: 99%

Single-Pixel Infrared Hyperspectral Imaging via Physics-Guided Generative Adversarial Networks

Wang,

Bie,

Chen

et al. 2024

Photonics

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A physics-driven generative adversarial network (GAN) was utilized to demonstrate a single-pixel hyperspectral imaging (HSI) experiment in the infrared spectrum, eliminating the need for extensive dataset training in most data-driven deep neural networks. Within the GAN framework, the physical process of single-pixel imaging (SPI) was integrated into the generator, and its estimated one-dimensional (1D) bucket signals and the actual 1D bucket signals were employed as constraints in the objective function to update the network’s parameters and optimize the generator with the assistance of the discriminator. In comparison to single-pixel infrared HSI methods based on compressive sensing and physics-driven convolution neural networks, our physics-driven GAN-based single-pixel infrared HSI exhibits superior imaging performance. It requires fewer samples and achieves higher image quality. We believe that our physics-driven network will drive practical applications in computational imaging, including various SPI-based techniques.

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“…It was first implemented using physical slits (masks), which blocked dispersed wavelengths of light. Since then, these masks have been replaced with digital-micro-mirror (DMD) devices for the multiplexing of light, ,, but it is also possible to multiplex by turning discrete wavelengths on and off should the light source consist of a battery of lasers or LEDs, for instance …”

Section: Introductionmentioning

confidence: 99%

Rapid Fluorescence EEM Spectroscopy Using Super-Cycle Hadamard-Transform Multiplexing

Ferguson,

Loock

2023

Anal. Chem.

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Hadamard-transform (HT) multiplexing has recently been applied to increasingly complex spectroscopic techniques. It had been shown that the data acquisition time for fluorescence excitation emission matrix (EEM) spectroscopy can be reduced by 1 or 2 orders of magnitude using HT multiplexing of the excitation light using a programmable light source. In these previous studies, the data acquisition rate had been limited by the time it took to record an EEM, that is, to complete one cycle of multiplexed excitation spectra. The extraction of chemical information, such as concentration and chemical identity, is then obtained from parallel factor (PARAFAC) analysis of the sequence of EEMs. In this contribution, we increase the data acquisition rate by another order of magnitude, limited ultimately by the time it takes to record a single excitation spectrum. Our algorithm is entirely based on improved data processing, that is, it can be applied to previously recorded HT multiplexed data sets. The algorithm is based on three previously unexplored approaches: (1) we perform a PARAFAC multivariate analysis on the raw (multiplexed) data set, (2) the time-independent PARAFAC loading vectors are obtained prior to obtaining the timedependent score vectors, and (3) when loading vectors are difficult to obtain from the EEMs, we instead use a rolling-average approach to considerably increase the stability of the fit. Analysis of experimental data shows that the scores of fluorescence EEMs with seven excitation wavelengths and over 1000 emission wavelengths can be obtained in less than 20 ms.

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Hadamard transform-based hyperspectral imaging using a single-pixel detector

Cited by 25 publications

References 28 publications

Computational ghost imaging with adaptive intensity illumination for scenes featuring specular surfaces

Computational ghost imaging with adaptive intensity illumination for scenes featuring specular surfaces

Single-Pixel Infrared Hyperspectral Imaging via Physics-Guided Generative Adversarial Networks

Rapid Fluorescence EEM Spectroscopy Using Super-Cycle Hadamard-Transform Multiplexing

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