Compressive Sensing Reconstruction for Video: An Adaptive Approach Based on Motion Estimation
IEEE Transactions on Circuits and Systems for Video Technology
Institute of Electrical and Electronics Engineers (IEEE)
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Ding, X., Chen, W., & Wassell, I. (2017). Compressive Sensing Reconstruction for Video: An Adaptive Approach Based on Motion Estimation. IEEE Transactions on Circuits and Systems for Video Technology https://doi.org/10.1109/TCSVT.2016.2540073
This paper focuses on the problem of causally reconstructing Compressive Sensing (CS) captured video. The state-of-art causal approaches usually assume the signal support is static or changing sufficiently slowly over time, where Magnetic Resonance Imaging (MRI) is widely used as a motivating example. However, such an assumption is too restrictive for many other video applications, where the signal support changes rapidly. In this paper, we propose a framework that combines Motion Estimation (ME), the Kalman Filter (KF) and CS to adapt the reconstruction process to motions in the video so that the slowly-changing assumption on the signal support is relaxed and consequently is more suitable for video reconstruction. Explicit and implicit ME are designed to provide motion aware predictions, upon which a modified KF procedure is applied. Furthermore, three CS algorithms with embedded ME and KF are developed, and theoretical analyses are conducted via reconstruction error upper bounds, to characterize the various factors that affect reconstruction accuracy. Extensive simulations utilizing actual videos are carried out and the superiority of our methods is demonstrated.
compressive sensing, Kalman filter, video reconstruction, motion estimation, multiscale reconstruction
This work is supported by EPSRC Research Grant EP/K033700/1; the Natural Science Foundation of China (61401018, U1334202).
Engineering and Physical Sciences Research Council (EP/K033700/1)
External DOI: https://doi.org/10.1109/TCSVT.2016.2540073
This record's URL: https://www.repository.cam.ac.uk/handle/1810/253970