A new compressive imaging camera architecture using optical-domain compression

By Dharmpal Takhar, Jason N. Laska, Michael B. Wakin, Marco F. Duarte, Dror Baron, Shriram Sarvotham, Kevin F. Kelly, Richard Baraniuk. Electronic imaging, 2006

641 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6199efbd2fa4a0e857a1c787e3e2a990395851bd

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In Biomedical Engineering

In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, methodology): “By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection.”
Block Compressed Sensing of Natural Images (2007, background): “Other choices of Φ can be found in [4,7].”
Sparsity and incoherence in compressive sampling (2006, methodology): “(It would be straightforward to use them in the imaging architecture proposed in [28], for example.”
An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (2010, neutral): “1: Single pixel camera block diagram [39].”
Compressive sensing: From theory to applications, a survey (2013, methodology): “The camera is based on a single photon detector adaptable to image at wavelengths which were impossible with conventional charge coupled device (CCD) and complementary metal oxide semiconductor (CMOS) images [53]–[55].”
Imaging with quantum states of light (2019, methodology): “[65] D.”
The smashed filter for compressive classification and target recognition (2007, positive): “Again assuming that Φ is an orthoprojector, the likelihood for each hypothesis Hi simply becomes p(y|θ,Hi) = 1 (2πσ) M 2 e 1 2σ ‖y−Φfi(θ)‖ 2 2 , (8)”
Sparse Representation for Computer Vision and Pattern Recognition (2010, background): “Sparsity and the design of appropriate dictionaries and projections have influenced the development of both algorithms and of physical imaging systems [6]–[8], [76].”
Iteratively reweighted algorithms for compressive sensing (2008, background): “A few of the many potential applications are medical image reconstruction [7], image acquisition [8], and sensor networks [9].”
An Architecture for Compressive Imaging (2006, methodology): “Our approach is based on a new digital image/video camera that directly acquires random projections without first collecting the N pixels/voxels [4].”
Statistical mechanics of complex neural systems and high dimensional data (2013, background): “There, diverse applications of how the techniques in sections 5 and 6 can be used to acquire and analyze high dimensional neuronal data are discussed, including magnetic resonance imaging [31]–[33], compressed gene expression arrays [34], compressed connectomics [35, 36], receptive field measurement”
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements (2016, methodology): “Thus, in effect, this testbed implements several single pixel cameras [29] in parallel.”