Imaging system of single pixel camera based on compressed sensing

Tao, 陈涛 Chen; Zheng-wei, 李正炜 Li; Jian-li, 王建立 Wang; Wang, Bin; Shuang, 郭爽 Guo

doi:10.3788/ope.20122011.2523

Cited by 5 publications

(1 citation statement)

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“…Candes [26], Tao [24] et al proposed that the measurement matrix needs to satisfy the Restricted Isometry Property (RIP) [25]. Gaussian random matrix

Φ

approximates the RIP [27,28,29], and is almost irrelevant to any sparse signal [30]. …”

Section: The Construction Of An Adaptive Measurement Matrixmentioning

confidence: 99%

Adaptive Single Photon Compressed Imaging Based on Constructing a Smart Threshold Matrix

Shangguan

Yan

Wang

et al. 2018

Sensors

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We demonstrate a single-photon compressed imaging system based on single photon counting technology and compressed sensing theory. In order to cut down the measurement times and shorten the imaging time, a fast and efficient adaptive sampling method, suited for single-photon compressed imaging, is proposed. First, the pre-measured rough images are transformed into sparse bases as a priori information. Then a smart threshold matrix is designed by using large sparse coefficients of the rough image in sparse bases. The adaptive measurement matrix is obtained by modifying the original Gaussian random matrix with the specially designed threshold matrix. Building the adaptive measurement matrix requires only one level of sparse representation, which means that adaptive imaging can be achieved quickly with very little computation. The experimental results show that the reconstruction effect of the image measured using the adaptive measurement matrix is obviously superior than that of the Gaussian random matrix under different measurement times and different reconstruction algorithms.

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“…Candes [26], Tao [24] et al proposed that the measurement matrix needs to satisfy the Restricted Isometry Property (RIP) [25]. Gaussian random matrix