Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing
Date:
Recommended citation: N. Diaz, C. Noriega-Wandurraga, A. Basarab, J. -. Tourneret and H. Arguello. “Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing” 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019 pp. 445-449. [Paper], [Link], [Slides].
This paper proposes a new motion estimation method based on convolutional sparse coding to adaptively design the colored-coded apertures in static and dynamic spectral videos. The motion in a spectral video is estimated from a low-resolution reconstruction of the datacube by training a convolutional dictionary per spectral band and solving a minimization problem. Simulations show improvements in terms of peak signal-to-noise ratio (of up to 2 dB) of the reconstructed videos by using the proposed approach, compared with state-of-art non-adaptive coded apertures.
Cite
@INPROCEEDINGS{9022649,
author={Diaz, N. and Noriega-Wandurraga, C. and Basarab, A. and Tourneret, J.-Y. and Arguello, H.},
booktitle={2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
title={Adaptive Coded Aperture Design by Motion Estimation using Convolutional Sparse Coding in Compressive Spectral Video Sensing},
year={2019},
volume={},
number={},
pages={445-449},
doi={10.1109/CAMSAP45676.2019.9022649}}