Compressive ultraspectral imaging using multiscale structured illumination

Kravets, Vladislav; Kondrashov, Paul; Stern, Adrian

doi:10.1364/ao.58.000f32

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…3 we show progressive CS of the USFA MTF chart taken by an SPC system (see Fig. 2 ) 18 . The USFA MTF target was taken by progressive compressive samples (see Progressive compressive sampling in the “ Methods ” section), where compressed Hadamard samples were added at each stage in order to improve the resolution.…”

Section: Resultsmentioning

confidence: 99%

“…We analyze our progressive CS method using numerical simulations and test it experimentally with a Single Pixel Camera (SPC) system. The SPC is very useful for applications such as infra-red imaging, microscopy, ultrasonic imaging, 3D LIDAR imaging, hyperspectral imaging and many more 1 , 18 – 25 . We demonstrate around × 30 improvement in reconstruction time and around 2.2 dB improvement in Peak Signal to Noise Ratio (PSNR) over the iterative reconstruction method in simulation studies on 512 by 512 test images, as well as a × 40 improvement in reconstruction time and around 1.8 dB improvement in PSNR on a 2048 by 2048 exemplary test image.…”

Section: Introductionmentioning

confidence: 99%

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Progressive compressive sensing of large images with multiscale deep learning reconstruction

Kravets

Stern

2022

Sci Rep

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Compressive sensing (CS) is a sub-Nyquist sampling framework that has been employed to improve the performance of numerous imaging applications during the last 15 years. Yet, its application for large and high-resolution imaging remains challenging in terms of the computation and acquisition effort involved. Often, low-resolution imaging is sufficient for most of the considered tasks and only a fraction of cases demand high resolution, but the problem is that the user does not know in advance when high-resolution acquisition is required. To address this, we propose a multiscale progressive CS method for the high-resolution imaging. The progressive sampling refines the resolution of the image, while incorporating the already sampled low-resolution information, making the process highly efficient. Moreover, the multiscale property of the progressively sensed samples is capitalized for a fast, deep learning (DL) reconstruction, otherwise infeasible due to practical limitations of training on high-resolution images. The progressive CS and the multiscale reconstruction method are analyzed numerically and demonstrated experimentally with a single pixel camera imaging system. We demonstrate 4-megapixel size progressive compressive imaging with about half the overall number of samples, more than an order of magnitude faster reconstruction, and improved reconstruction quality compared to alternative conventional CS approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Progressive compressive sensing of large images with multiscale deep learning reconstruction

Kravets

Stern

2022

Sci Rep

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“…MISO-GI will become a promising scheme for high-speed GI and has the potential to be extended to hyperspectral GI and polarized GI. In addition, compressed sensing [27] algorithm has been used in GI schemes [28]- [31] to reconstruct a high quality image of object and to reduce both the TST and the number of speckle patterns significantly by the aid of the redundant structure of the images. Hence, the 2C algorithm and the FWHT algorithm can be replaced by compressed sensing algorithm in the MISO-GI scheme to further reduce the TST.…”

Section: Resultsmentioning

confidence: 99%

Multiple-Input Single-Output Ghost Imaging

Wang

Zhao

2020

IEEE Photonics J.

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In this paper, we propose a multiple-input single-output ghost imaging scheme, named MISO-GI. In the scheme, K spatiotemporal speckle pattern sources (multiple input) simultaneously generate K different spatiotemporal modulated speckle patterns which then are illuminated on an unknown object at the same time. Then, a non-spatial-resolution bucket detector (single output) is used to detect the light field interacting with the object. According to the orthogonality of speckle patterns from different spatiotemporal speckle pattern sources, the detection signal is separated into K components corresponding to K spatiotemporal modulated speckle patterns. With the effective recovery algorithm, an image is reconstructed by using all spatiotemporal modulated speckle patterns and their corresponding components of detection signals. Both experimental and simulation results verify the feasibility of the proposed MISO-GI scheme. Moreover, the results also show that the proposed MISO-GI scheme could reduce the total sampling time by K times and improve the imaging quality comparing to traditional GI, without the decrease of the number of speckle patterns and the picture time as well as the consumption of other light field dimensions. It provides a promising avenue to realize GI with limited sampling time and has potential for more applications.

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“…In practical terms, this would correspond to a 92% shorter acquisition time, corresponding to a 12.5× speed up of the image reconstruction process when compared to a full scan based on the Hadamard encoding scheme. These numbers suggest that the reconstruction process could be significantly sped up through the application of adaptive-basis-scan algorithms and deep-learning-enhanced imaging, which identify and predict the best set of scanning patterns in real time [40,[49][50][51].…”

Section: Discussionmentioning

confidence: 99%

“…The Walsh ordering is particularly useful in image reconstruction as it mirrors the standard order of the discrete Fourier basis, i.e., the columns are sorted in terms of increasing spatial frequencies. This means that by using the Walsh matrix, it is possible to acquire complete lower-resolution images before completing the illumination set, which can be useful for applying decisional approaches and reducing the set dimension [39,40]. Each pattern is defined as the tensor product between two columns of the generating matrix.…”

Section: Compressed and Adaptive Sensing Applicationsmentioning

confidence: 99%

Route to Intelligent Imaging Reconstruction via Terahertz Nonlinear Ghost Imaging

et al. 2020

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Terahertz (THz) imaging is a rapidly emerging field, thanks to many potential applications in diagnostics, manufacturing, medicine and material characterisation. However, the relatively coarse resolution stemming from the large wavelength limits the deployment of THz imaging in micro- and nano-technologies, keeping its potential benefits out-of-reach in many practical scenarios and devices. In this context, single-pixel techniques are a promising alternative to imaging arrays, in particular when targeting subwavelength resolutions. In this work, we discuss the key advantages and practical challenges in the implementation of time-resolved nonlinear ghost imaging (TIMING), an imaging technique combining nonlinear THz generation with time-resolved time-domain spectroscopy detection. We numerically demonstrate the high-resolution reconstruction of semi-transparent samples, and we show how the Walsh–Hadamard reconstruction scheme can be optimised to significantly reduce the reconstruction time. We also discuss how, in sharp contrast with traditional intensity-based ghost imaging, the field detection at the heart of TIMING enables high-fidelity image reconstruction via low numerical-aperture detection. Even more striking—and to the best of our knowledge, an issue never tackled before—the general concept of “resolution” of the imaging system as the “smallest feature discernible” appears to be not well suited to describing the fidelity limits of nonlinear ghost-imaging systems. Our results suggest that the drop in reconstruction accuracy stemming from non-ideal detection conditions is complex and not driven by the attenuation of high-frequency spatial components (i.e., blurring) as in standard imaging. On the technological side, we further show how achieving efficient optical-to-terahertz conversion in extremely short propagation lengths is crucial regarding imaging performance, and we propose low-bandgap semiconductors as a practical framework to obtain THz emission from quasi-2D structures, i.e., structure in which the interaction occurs on a deeply subwavelength scale. Our results establish a comprehensive theoretical and experimental framework for the development of a new generation of terahertz hyperspectral imaging devices.

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Compressive ultraspectral imaging using multiscale structured illumination

Cited by 6 publications

References 30 publications

Progressive compressive sensing of large images with multiscale deep learning reconstruction

Progressive compressive sensing of large images with multiscale deep learning reconstruction

Multiple-Input Single-Output Ghost Imaging

Route to Intelligent Imaging Reconstruction via Terahertz Nonlinear Ghost Imaging

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