TY - GEN
T1 - Temporal compressive imaging for video
AU - Zhou, Qun
AU - Zhang, Linxia
AU - Ke, Jun
N1 - Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - In many situations, imagers are required to have higher imaging speed, such as gunpowder blasting analysis and observing high-speed biology phenomena. However, measuring high-speed video is a challenge to camera design, especially, in infrared spectrum. In this paper, we reconstruct a high-frame-rate video from compressive video measurements using temporal compressive imaging (TCI) with a temporal compression ratio T=8. This means that, 8 unique high-speed temporal frames will be obtained from a single compressive frame using a reconstruction algorithm. Equivalently, the video frame rates is increased by 8 times. Two methods, two-step iterative shrinkage/threshold (TwIST) algorithm and the Gaussian mixture model (GMM) method, are used for reconstruction. To reduce reconstruction time and memory usage, each frame of size 256 × 256 is divided into patches of size 8×8. The influence of different coded mask to reconstruction is discussed. The reconstruction qualities using Twist and GMM are also compared.
AB - In many situations, imagers are required to have higher imaging speed, such as gunpowder blasting analysis and observing high-speed biology phenomena. However, measuring high-speed video is a challenge to camera design, especially, in infrared spectrum. In this paper, we reconstruct a high-frame-rate video from compressive video measurements using temporal compressive imaging (TCI) with a temporal compression ratio T=8. This means that, 8 unique high-speed temporal frames will be obtained from a single compressive frame using a reconstruction algorithm. Equivalently, the video frame rates is increased by 8 times. Two methods, two-step iterative shrinkage/threshold (TwIST) algorithm and the Gaussian mixture model (GMM) method, are used for reconstruction. To reduce reconstruction time and memory usage, each frame of size 256 × 256 is divided into patches of size 8×8. The influence of different coded mask to reconstruction is discussed. The reconstruction qualities using Twist and GMM are also compared.
KW - Gaussian mixture model
KW - Image reconstruction
KW - Temporal compressive imaging
KW - Twist algorithm
UR - http://www.scopus.com/inward/record.url?scp=85040990326&partnerID=8YFLogxK
U2 - 10.1117/12.2288148
DO - 10.1117/12.2288148
M3 - Conference contribution
AN - SCOPUS:85040990326
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - 2017 International Conference on Optical Instruments and Technology
A2 - Situ, Guohai
A2 - Osten, Wolfgang
A2 - Cao, Xun
PB - SPIE
T2 - 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, OIT 2017
Y2 - 28 October 2017 through 30 October 2017
ER -