Single disperser design for coded aperture snapshot spectral imaging.

By Ashwin A. Wagadarikar, Renu John, Rebecca M. Willett, David J. Brady. Applied Optics, 2008

962 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Biophysics, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/3a49c99acb708afe1e7500824af5a5eeafd96810

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Citations in the corpus

Rank Minimization for Snapshot Compressive Imaging (2018, methodology): “We validate the proposed DeSCI methods on two typical snapshot compressive imaging systems, i.e. , snapshot Objective lens Mask/DMD Relay lens Mono/color CCD Scene Fig.”
Kronecker Compressive Sensing (2012, background): “Whil e g obal measurements that multiplex the entire set of data have been proposed [5, 7, 13, 17], practical architectures that provide such measurements are rare [5].”
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing (2009, background): “Two of the early important applications of CS are in MRI [10] and in development of new hyperspectral cameras [11].”
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (2021, methodology): “Traditional HSI reconstruction methods [16, 21, 26, 27, 43, 46, 52, 57, 62] are mainly based on hand-crafted priors.”
Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021, methodology): “The elegant design of CASSI (spectral SCI) used passive modulation, and thus it has a low cost, and no additional power is required; the path to integrate it into mobile devices is clear (by making it small).”
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images (2018, methodology): “To overcome these difficulties and enable hyperspectral image acquisition in dynamic conditions, a number of solutions based on compressed sensing are proposed by encoding the spectral information, which transfer the cost from capture to computational reconstruction [26, 34, 35, 36].”
High-quality hyperspectral reconstruction using a spectral prior (2017, methodology): “Our novel spectral reconstruction algorithm can be applied to captured input from any compressive imaging technique, e.g., SD-CASSI, SS-CASSI, and DD-CASSI, providing better results with a significant speed-up factor over other existing data-driven approaches.”
Structured Compressed Sensing: From Theory to Applications (2011, background): “Note that the survey of CS devices given in this section is by no means exhaustive [66]–[68]; our focus is on CS matrices that have been investigated from both a theoretical and an implementational point of view.”
Compressive Coded Aperture Spectral Imaging: An Introduction (2014, methodology): “In its simplest form, CASSI measurements are realized optically by a coded aperture, a dispersive element such as a prism, and an FPA detector [1], [3].”
Optical Imaging and Spectroscopy (2009, methodology): “For example, since the number of spectral channels is modest and high image quality and quantum efficiency is paramount, current CASSI designs use prism-based dispersion elements rather than diffractive gratings [242].”
Toward real-time terahertz imaging (2018, background): “A second grating perpendicular to the first one can be added to map the frequencies in 2D spatial coordinates [397,398].”
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (2022, background): “To address these limitations, researchers have developed SCI systems [18, 26, 50, 71, 72] to capture HSIs, where the 3D HSI cube is compressed into a single 2D measurement [81].”