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Rethinking Compressive Sensing
2018 26th European Signal Processing Conference (EUSIPCO), 2018In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for
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On Compressive orthonormal Sensing
2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016The Compressive Sensing (CS) approach for recovering sparse signal with orthonormal measurements has been studied under various notions of coherence. However, existing notions of coherence either do not exploit the structure of the underlying signal, or are too complicated to provide an explicit sampling scheme for all orthonormal basis sets ...
Yi Zhou 0017 +2 more
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Near-Optimal Compression for Compressed Sensing
2015 Data Compression Conference, 2015In this note we study the under-addressed quantization stage implicit in any compressed sensing signal acquisition paradigm. We also study the problem of compressing the bit-stream resulting from the quantization. We propose using Sigma-Delta (a#x03A3;a#x0394;) quantization followed by a compression stage comprised of a discrete Johnson-Linden Strauss ...
Rayan Saab +2 more
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Spatiotemporal compressed sensing for video compression
2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 2017We present a hardware-friendly spatiotemporal compressed sensing framework for video compression. The spatiotemporal compressed sensing incorporates random sampling in both spatial and temporal domain to encode the video scene into a single coded image. During decoding, the video is reconstructed using dictionary learning and sparse recovery.
Tao Xiong +6 more
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Using compression codes in compressed sensing
2016 IEEE Information Theory Workshop (ITW), 2016Data compression and compressed sensing algorithms exploit the structure present in a signal for its efficient representation and measurement, respectively. While most state-of-the-art data compression codes take advantage of complex patterns present in signals of interest, this is not the case in compressed sensing.
Farideh Ebrahim Rezagah +3 more
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Compressing YOLO Network by Compressive Sensing
2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), 2017Object detection is one of the fundamental challenges in pattern recognition community. Recently, convolutional neural networks (CNN) are increasingly exploited in object detection, showing their promising potentials of generatively discovering patterns from quantity of labeled images.
Yirui Wu +3 more
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Compressive sensing for space image compressing
Proceedings of the 2016 International Conference on Intelligent Information Processing, 2016Compressive sensing is a new technique by which sparse signals are sampled and recovered from a few measurements. To address the disadvantages of traditional space image compressing methods, a complete new compressing scheme under the compressive sensing framework was developed in this paper.
Zheng Li +3 more
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Terahertz compressed sensing imaging based on line array detection
Optics and Lasers in Engineering, 2023Xiaoxue Hu, Zehui Lu, Sixing Xi
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Image encryption based on compressed sensing and DNA encoding
Signal Processing: Image Communication, 2021Yining Su
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