The Experts below are selected from a list of 391557 Experts worldwide ranked by ideXlab platform
Gonzalo R. Arce - One of the best experts on this subject based on the ideXlab platform.
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Snapshot spectral imaging via compressive random Convolution
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011Co-Authors: Y.-R.a b Wu, Gonzalo R. ArceAbstract:Spectral imaging is of interest in many applications, including wide-area airborne surveillance, remote sensing, and tissue spectroscopy. Coded aperture spectral snapshot imaging (CASSI) provides an efficient mechanism to capture a 3D spectral cube with a single shot 2D measurement. CASSI uses a focal plane array (FPA) measurement of a spectrally dispersed, aperture coded, source. The spectral cube is then attained using a compressive sensing reconstruction algorithm. In this paper, we explore a new approach referred to as random Convolution snapshot spectral imaging (RCSSI). It is based on FPA measurements of spectrally dispersed coherent sources that have been randomly convoluted by a spatial light modulator. The new method, based on the theory of compressive sensing via random Convolutions, is shown to outperform traditional CASSI systems in terms of PSNR spectral image cube reconstructions. © 2011 IEEE.
Jianmin Jiang - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Image Super-Resolution Using Recursive Depthwise Separable Convolution Network
IEEE Access, 2019Co-Authors: Kwok-wai Hung, Zhikai Zhang, Jianmin JiangAbstract:In recent years, deep Convolutional neural networks (CNNs) have been widely used for image super-resolution (SR) to achieve a range of sophisticated performances. Despite the significant advancement made in CNNs, it is still difficult to apply CNNs to practical SR applications due to enormous computations of deep Convolutions. In this paper, we propose two lightweight deep neural networks using depthwise separable Convolution for the real-time image SR. Specifically, depthwise separable Convolution divides the standard Convolution into depthwise Convolution and pointwise Convolution to significantly reduce the number of model parameters and multiplication operations. Moreover, recursive learning is adopted to increase the depth and receptive field of the network in order to improve the SR quality without increasing the model parameters. Finally, we propose a novel technique called Super-Sampling (SS) to learn more abundant high-resolution information by over-sampling the output image followed by adaptive down-sampling. The proposed two models, named SSNet-M and SSNet, outperform the existing state-of-the-art real-time image SR networks, including SRCNN, FSRCNN, ESPCN, and VDSR, in terms of model complexity, and subjective and PSNR/SSIM evaluations on Set5, Set14, B100, Urban100, and Manga109.
Alain Artieri - One of the best experts on this subject based on the ideXlab platform.
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A new VLSI architecture for large kernel real time Convolution
International Conference on Acoustics Speech and Signal Processing, 1990Co-Authors: Francis Jutand, Nicolas Demassieux, Alain ArtieriAbstract:A VLSI architecture is introduced to achieve a single-chip real-time implementation of large-kernel Convolutions. The architecture provides a way to organize the computation in order to lower the I/O bandwidth to 2 pixels per clock cycle, without increasing the internal storage. As a result, the whole silicon array can be dedicated to computation, without excessive external memory requirements, opening the way to single-chip, very-large-kernel Convolutions. As an example, a 16×16 Convolution or correlation architecture has been devised based on a 1.2-μm CMOS process. The same architecture can be used for data processing involving 2-D data convergence
Eckhard Hitzer - One of the best experts on this subject based on the ideXlab platform.
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Space-Time Fourier Transform, Convolution and Mustard Convolution
2020Co-Authors: Eckhard HitzerAbstract:In this paper we use the steerable space-time Fourier transform (SFT), and relate the classical Convolution of the algebra for spacetime Cl(3, 1)-valued signals over the space-time vector space R, with the (equally steerable) Mustard Convolution. A Mustard Convolution can be expressed in the spectral domain as the point wise product of the SFTs of the factor functions. In full generality do we express the classical Convolution of space-time signals in terms of finite linear combinations of Mustard Convolutions, and vice versa the Mustard Convolution of space-time signals in terms of finite linear combinations of classical Convolutions.
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General Steerable Two-sided Clifford Fourier Transform, Convolution and Mustard Convolution
Advances in Applied Clifford Algebras, 2016Co-Authors: Eckhard HitzerAbstract:In this paper we use the general steerable two-sided Clifford Fourier transform (CFT), and relate the classical Convolution of Clifford algebra-valued signals over \({\mathbb{R}^{p,q}}\) with the (equally steerable) Mustard Convolution. A Mustard Convolution can be expressed in the spectral domain as the point wise product of the CFTs of the factor functions. In full generality we express the classical Convolution of Clifford algebra signals in terms of finite linear combinations of Mustard Convolutions, and vice versa the Mustard Convolution of Clifford algebra signals in terms of finite linear combinations of classical Convolutions.
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General two-sided quaternion Fourier transform, Convolution and Mustard Convolution
Advances in Applied Clifford Algebras, 2016Co-Authors: Eckhard HitzerAbstract:In this paper we use the general two-sided quaternion Fourier transform (QFT), and relate the classical Convolution of quaternion-valued signals over \({{\mathbb R}^2}\) with the Mustard Convolution. A Mustard Convolution can be expressed in the spectral domain as the point wise product of the QFTs of the factor functions. In full generality do we express the classical Convolution of quaternion signals in terms of finite linear combinations of Mustard Convolutions, and vice versa the Mustard Convolution of quaternion signals in terms of finite linear combinations of classical Convolutions.
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General Two-Sided Clifford Fourier Transform, Convolution and Mustard Convolution
viXra, 2016Co-Authors: Eckhard HitzerAbstract:In this paper we use the general steerable two-sided Clifford Fourier transform (CFT), and relate the classical Convolution of Clifford algebra-valued signals over $\R^{p,q}$ with the (equally steerable) Mustard Convolution. A Mustard Convolution can be expressed in the spectral domain as the point wise product of the CFTs of the factor functions. In full generality do we express the classical Convolution of Clifford algebra signals in terms of finite linear combinations of Mustard Convolutions, and vice versa the Mustard Convolution of Clifford algebra signals in terms of finite linear combinations of classical Convolutions.
Y.-R.a b Wu - One of the best experts on this subject based on the ideXlab platform.
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Snapshot spectral imaging via compressive random Convolution
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011Co-Authors: Y.-R.a b Wu, Gonzalo R. ArceAbstract:Spectral imaging is of interest in many applications, including wide-area airborne surveillance, remote sensing, and tissue spectroscopy. Coded aperture spectral snapshot imaging (CASSI) provides an efficient mechanism to capture a 3D spectral cube with a single shot 2D measurement. CASSI uses a focal plane array (FPA) measurement of a spectrally dispersed, aperture coded, source. The spectral cube is then attained using a compressive sensing reconstruction algorithm. In this paper, we explore a new approach referred to as random Convolution snapshot spectral imaging (RCSSI). It is based on FPA measurements of spectrally dispersed coherent sources that have been randomly convoluted by a spatial light modulator. The new method, based on the theory of compressive sensing via random Convolutions, is shown to outperform traditional CASSI systems in terms of PSNR spectral image cube reconstructions. © 2011 IEEE.
