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Analysis and utility of atmospheric compensation of simulated compressive sensing (CS) measurements
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Compressive sensing (CS) takes advantage of the spatial and spectral redundancy in hyperspectral imagery to take fewer measurements than traditional sensors. We simulate compressively sensed hyperspectral airborne images of a HyMap image of Cooke City, Montana using the Coded Aperture Snapshot Spectral Imager Dual Disperser (CASSI-DD) sensor model ...
Maria Busuioceanu +3 more
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MIM-CS: Message Importance Measure for Compressed Sensing
2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), 2021Yuchen Shi +3 more
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2018
A hybrid compression method based on compressive sensing (CS) theory proposed for various biometric data and biomedical data in this paper. The data compression method is designed using CS theory, discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). In this method, first DCT and then DWT are applied
Rohit Thanki +2 more
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A hybrid compression method based on compressive sensing (CS) theory proposed for various biometric data and biomedical data in this paper. The data compression method is designed using CS theory, discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). In this method, first DCT and then DWT are applied
Rohit Thanki +2 more
openaire +1 more source
Radiological Physics and Technology
This study aimed to compare the relationship between the quantitative values and visual score of acquired images using the CS-SENSE method. T1-weighted image (T1WI) and T2-weighted image (T2WI) were acquired using a phantom created by a 3D printer.
Norikazu Koori +12 more
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This study aimed to compare the relationship between the quantitative values and visual score of acquired images using the CS-SENSE method. T1-weighted image (T1WI) and T2-weighted image (T2WI) were acquired using a phantom created by a 3D printer.
Norikazu Koori +12 more
openaire +2 more sources
2018
One important research point of compressive sensing (CS) is to restore a high-dimensional signal as completely as possible from its compressed form, which has much lower dimensionality than the original. Several methods have been employed to this end, including traditional iterative methods as well as recurrent approaches based on deep learning.
Wentao Wan, Guohui Li, Peng Pan
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One important research point of compressive sensing (CS) is to restore a high-dimensional signal as completely as possible from its compressed form, which has much lower dimensionality than the original. Several methods have been employed to this end, including traditional iterative methods as well as recurrent approaches based on deep learning.
Wentao Wan, Guohui Li, Peng Pan
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Compressive Sensing (CS) on Wireless Sensor Network for Manufacturing Process Monitoring
2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), 2023Muhammad Chaerullah +2 more
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2022
F Siedek +6 more
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F Siedek +6 more
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Deep Seismic CS: A Deep Learning Assisted Compressive Sensing for Seismic Data
IEEE Transactions on Geoscience and Remote Sensing, 2023Naveed Iqbal +3 more
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