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FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
, 2015
"... FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light f ..."
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FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. FlatCam is an instance of a coded aperture imaging system; however, unlike the vast majority of related work, we place the coded mask extremely close to the image sensor that can enable a thin system. We employ a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of the FlatCam design using two prototypes: one at visible wavelengths and one at infrared wavelengths.
1FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
"... FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light f ..."
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FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. FlatCam is an instance of a coded aperture imaging system; however, unlike the vast majority of related work, we place the coded mask extremely close to the image sensor that can enable a thin system. We employ a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of the FlatCam design using two prototypes: one at visible wavelengths and one at infrared wavelengths. I.
THEME Audio, Speech, and Language ProcessingTable of contents
"... Speech and sound data modeling and processing IN COLLABORATION WITH: Institut de recherche en informatique et systèmes aléatoires (IRISA) ..."
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Speech and sound data modeling and processing IN COLLABORATION WITH: Institut de recherche en informatique et systèmes aléatoires (IRISA)
Project-Team METISS Modélisation et Expérimentation pour le Traitement des Informations et des Signaux
"... c t i v it y e p o r t 2008 Table of contents ..."
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Filter Design for a Compressive Sensing Delay and Doppler Estimation Framework
"... Abstract—The theory of compressive sensing (CS) aims to find efficient signal acquisition and recovery techniques with the aid of prior knowledge about the signal. While traditionally applied to sparse vectors, CS has been extended to analog signals with more general structures. The use of CS in del ..."
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Abstract—The theory of compressive sensing (CS) aims to find efficient signal acquisition and recovery techniques with the aid of prior knowledge about the signal. While traditionally applied to sparse vectors, CS has been extended to analog signals with more general structures. The use of CS in delay and Doppler estimation in radar application has recently received attention from the signal processing community. In this paper, we adopt one of the available CS frameworks for delay and Doppler estimation and optimize the deployed filter in this framework. The optimization criterion is the Bayesian Crámer Rao Bound of delay estimation and Doppler in general additive Gaussian interference. An iterative algorithm is proposed to solve the optimization problem and the results are compared with the prototype filter design available in the literature. I.
Image Classification with A Deep Network Model based on Compressive Sensing
"... Abstract—To simplify the parameter of the deep learning network, a cascaded compressive sensing model “CSNet ” is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the data. Secondly, CSNet generates the feature by binary hashing and blo ..."
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Abstract—To simplify the parameter of the deep learning network, a cascaded compressive sensing model “CSNet ” is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the data. Secondly, CSNet generates the feature by binary hashing and block-wise histograms. Finally, a linear SVM classifier is used to classify these features. The experiments on the MNIST dataset indicate that higher classification accuracy can be obtained by this algorithm.
