Compressive sensing spectroscopy with a single pixel camera

Starling, David J.; Storer, Ian; Howland, Gregory A.

doi:10.1364/ao.55.005198

Cited by 22 publications

(15 citation statements)

References 25 publications

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“…Until now, the DMD has been applied in various fields, including analytical atomic spectrometry [125], chemical sensing [126] and infrared sensing [127]. Besides, CS theory was also utilized to reduce the required number of measurements, thus significantly reducing the sampling time [128,129]. It should be noted that the real resolution of a spectrometric system is also dependent on the optical set-up, so some improvement and analysis have been done by modifying the mask pattern widths to improve the performance of the DMD-based spectrometers in near-infrared sensing applications [130].…”

Section: One Dimensional Imaging: Spectrometermentioning

confidence: 99%

Single-Pixel MEMS Imaging Systems

Zhou

Lim

2020

Micromachines

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Single-pixel imaging technology is an attractive technology considering the increasing demand of imagers that can operate in wavelengths where traditional cameras have limited efficiency. Meanwhile, the miniaturization of imaging systems is also desired to build affordable and portable devices for field applications. Therefore, single-pixel imaging systems based on microelectromechanical systems (MEMS) is an effective solution to develop truly miniaturized imagers, owing to their ability to integrate multiple functionalities within a small device. MEMS-based single-pixel imaging systems have mainly been explored in two research directions, namely the encoding-based approach and the scanning-based approach. The scanning method utilizes a variety of MEMS scanners to scan the target scenery and has potential applications in the biological imaging field. The encoding-based system typically employs MEMS modulators and a single-pixel detector to encode the light intensities of the scenery, and the images are constructed by harvesting the power of computational technology. This has the capability to capture non-visible images and 3D images. Thus, this review discusses the two approaches in detail, and their applications are also reviewed to evaluate the efficiency and advantages in various fields.

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Section: One Dimensional Imaging: Spectrometermentioning

confidence: 99%

Single-Pixel MEMS Imaging Systems

Zhou

Lim

2020

Micromachines

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“…Compared to CMOS or CCD cameras, the SPI camera has several outstanding advantages: (1) the single-point detector is typically sensitive to weak light intensity change [10]; hence, it is efficient; (2) using an SPI device has intrinsic hardware compression [11]; therefore, it just requires small memory; (3) the cost of building a single-pixel camera is much lower than that of an ordinary camera [12], so it is economical, as well. As a result, the SPI architecture is of interest for various applications, such as color imaging [13], Terahertz imaging [14], infrared imaging [15], hyperspectral imaging [16], and video acquisition [17].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Pattern Array for Single-Pixel Imaging

et al. 2019

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Single-pixel imaging (SPI) is an emerging framework that can capture the image of a scene via a single-point detector at a considerably low cost. It measures the projection at the detector of the scene under view with certain patterns. One can reconstruct the image of the scene via post-processing the measurements modulated by the patterns. However, the most commonly-used random patterns are not always desirable in many applications, especially for real-time, resource-limited occasions, due to their high memory requirement and huge cost in software and hardware implementation. In this paper, a chaotic pattern array is proposed for the SPI architecture. Compared with random patterns, the proposed chaotic pattern array can not only promise to increase the capabilities of the SPI device, but can also reduce the memory cost and complexity of hardware implementation in the meantime. Moreover, convincing experiment results are given to illustrate that the proposed pattern array is suitable for single-pixel cameras, as well as other compressive imaging applications.

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“…Indirect compressive imaging techniques termed as single pixel imaging and computational ghost imaging [1,2] contribute to many novel ideas in optics. Prospect applications of these measurement methods include spectral imaging [3,4,5,6], polarimetric imaging [7,8,9], 3D imaging [10,11,12], around-the-corner imaging [13], imaging through scattering media [14], imaging at low lightlevels [15], spectroscopy [16], pattern recognition [17] etc. The inherently indirect principle of the measurement in single pixel detectors makes it necessary to reconstruct the image numerically after the measurement.…”

Section: Introductionmentioning

confidence: 99%

Real-time single-pixel video imaging with Fourier domain regularization

2018

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We present a closed-form image reconstruction method for single-pixel imaging based on the generalized inverse of the measurement matrix. Its numerical cost scales proportionally with the number of measured samples. Regularization of the inverse problem is obtained by minimizing the norms of the convolution between the reconstructed image and a set of spatial filters. The final reconstruction formula can be expressed in terms of matrix pseudoinverse. At high compression, this approach is an interesting alternative to the methods of compressive sensing based on l1-norm optimization, which are too slow for real-time applications. For instance, we demonstrate experimental single-pixel detection with real-time reconstruction obtained in parallel with measurement at a frame rate of 11 Hz for highly compressive measurements with a resolution of 256 × 256. To this end, we preselect the sampling functions to match the average spectrum obtained with an image database. The sampling functions are selected from the Walsh-Hadamard basis, from the discrete cosine basis, or from a subset of Morlet wavelets convolved with white noise. We show that by incorporating the quadratic criterion into the closed-form reconstruction formula, we can use binary rather than continuous sampling and reach similar reconstruction quality as is obtained by minimizing the total variation. This makes it possible to use cosine- or Morlet-based sampling with digital micromirror devices without advanced binarization methods.

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Compressive sensing spectroscopy with a single pixel camera

Cited by 22 publications

References 25 publications

Single-Pixel MEMS Imaging Systems

Single-Pixel MEMS Imaging Systems

Chaotic Pattern Array for Single-Pixel Imaging

Real-time single-pixel video imaging with Fourier domain regularization

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