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

• Main Authors: Wentao Shangguan, Qiurong Yan, Hui Wang, Chenglong Yuan, Bing Li, Yuhao Wang
• Format: Article
• Language: English
• Published: MDPI AG 2018-10-01
• Series: Sensors
• Subjects: adaptive sensing; adaptive signal detection; compressed sensing; image sampling; measurement matrix; single-photon compressed imaging
• Online Access: http://www.mdpi.com/1424-8220/18/10/3449

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.