Multispectral principal component imaging

Pal, Himadri S.; Neifeld, Mark A.

doi:10.1364/oe.11.002118

Cited by 18 publications

(7 citation statements)

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“…Charles et al (2011) and Chakrabarti and Zickler (2011) have extensively exploited the sparsity of spectrum data, and proposed spectrum dictionary-based imaging methods. In addition to coded aperture-based hyperspectral imaging, researchers have further effectively exploited the compressibility of spectrum data based on micromirror arrays, principal component imaging (Pal and Neifeld, 2003), feature-specific imaging (Neifeld and Shankar, 2003), fluorescence imaging (Suo et al, 2014), and spatial-temporal coding (Lin et al, 2014).…”

Section: Spectral Dimensionmentioning

confidence: 99%

Emerging theories and technologies on computational imaging

Suo

et al. 2017

Frontiers Inf Technol Electronic Eng

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Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.

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Section: Spectral Dimensionmentioning

confidence: 99%

Emerging theories and technologies on computational imaging

Suo

et al. 2017

Frontiers Inf Technol Electronic Eng

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“…It should be noted that to satisfy the photon constraint, 18 P must be multiplied by a scalar value such that the L1 norm of its columns is less than or equal to unity. Specifically, the measurement matrix is multiplied by a scalar value to normalize the largest column L1 norm to 1.0, which also ensures that the rest of the columns do not violate the photon constraint.…”

Section: Image Reconstruction Algorithmsmentioning

confidence: 99%

“…Boundary artifacts between adjoining blocks are less prominent when the matrices are normalized to satisfy the "photon constraint" 18 to account for finite measurement resources (such as integration time or equivalently the number of photons that are available during each measurement).…”

Section: Introductionmentioning

confidence: 99%

High-resolution imaging using a translating coded aperture

et al. 2017

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Abstract. It is well known that a translating mask can optically encode low-resolution measurements from which higher resolution images can be computationally reconstructed. We experimentally demonstrate that this principle can be used to achieve substantial increase in image resolution compared to the size of the focal plane array (FPA). Specifically, we describe a scalable architecture with a translating mask (also referred to as a coded aperture) that achieves eightfold resolution improvement (or 64∶1 increase in the number of pixels compared to the number of focal plane detector elements). The imaging architecture is described in terms of general design parameters (such as field of view and angular resolution, dimensions of the mask, and the detector and FPA sizes), and some of the underlying design trades are discussed. Experiments conducted with different mask patterns and reconstruction algorithms illustrate how these parameters affect the resolution of the reconstructed image. Initial experimental results also demonstrate that the architecture can directly support task-specific information sensing for detection and tracking, and that moving objects can be reconstructed separately from the stationary background using motion priors.

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“…However, unlike those methods, the lack of information measurement is intentional and has a specifically-designed structure. Our approach draws on the recent success of compressive sensing [6][7][8][15][16][17][18][19][20][21]. The ability to solve such an underdetermined problem relies on the properties of "natural" signals-specifically that they tend to be sparse in some basis other than the naïve Dirac sampling basis.…”

Section: Imaging System Design 21 Design Motivationmentioning

confidence: 99%

Multiscale reconstruction for computational spectral imaging

2007

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In this work we develop a spectral imaging system and associated reconstruction methods that have been designed to exploit the theory of compressive sensing. Recent work in this emerging field indicates that when the signal of interest is very sparse (i.e. zero-valued at most locations) or highly compressible in some basis, relatively few incoherent observations are necessary to reconstruct the most significant non-zero signal components. Conventionally, spectral imaging systems measure complete data cubes and are subject to performance limiting tradeoffs between spectral and spatial resolution. We achieve single-shot full 3D data cube estimates by using compressed sensing reconstruction methods to process observations collected using an innovative, real-time, dual-disperser spectral imager. The physical system contains a transmissive coding element located between a pair of matched dispersers, so that each pixel measurement is the coded projection of the spectrum in the corresponding spatial location in the spectral data cube. Using a novel multiscale representation of the spectral image data cube, we are able to accurately reconstruct 256 × 256 × 15 spectral image cubes using just 256 × 256 measurements.Keywords: wavelets, compressed sensing, hyperspectral imaging COMPRESSIVE SPECTRAL IMAGINGSpectral imaging is a highly effective tool in a variety of scientific and engineering contexts because of the information it implicitly encodes about the nature of the materials being imaged. While traditional digital imaging techniques produce images with scalar values associated with each spatial pixel location, in spectral images these scalar values are replaced with a vector containing the spectral information from that spatial location. The resulting data is therefore three-dimensional (two spatial dimensions and one spectral dimension). Spectral information can be vital for tasks such as monitoring the health of a forest ecosystem [1,2], increasing our understanding of solar physics [3], or examining tissue and organisms used to study cellular dynamics [4, 5].Despite its potential, however, many modern spectral imagers face a limiting tradeoff between spatial and spectral resolution, with the total number of voxels measured constrained by the size of the detector array. This constraint limits the utility and cost-effectiveness of spectral imaging for many applications. Furthermore, straightforward application of traditional spectroscopic techniques to spectral imaging proves problematic. The simplest spectral imager forms combine either a pushbroom (linear scanning) or tomographic (rotational scanning) front-end with a traditional slit-based dispersive spectrometer. Unfortunately, in many of the most interesting applications (most notably biophotonics) the sources are often both spatially-incoherent and weak. Spatially-incoherent sources present a significant challenge to slit-based dispersive spectrometers, and extremely poor photon collection efficiency is the result. When the source is also weak, the abso...

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Multispectral principal component imaging

Cited by 18 publications

References 0 publications

Emerging theories and technologies on computational imaging

Emerging theories and technologies on computational imaging

High-resolution imaging using a translating coded aperture

Multiscale reconstruction for computational spectral imaging

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