Improved Video Reconstruction Basing on Single-Pixel Camera By Dual-Fiber Collecting
The single-pixel camera is a new architecture of camera proposed in recent years. The difference between a traditional camera and a single-pixel camera is that one image can be reconstructed by acquiring less amount of data with the latter. Most existing single-pixel cameras only collect data for one light path. In this paper, in order to reduce the impact of measurement noise, we adopt a way of dual-fiber acquisition to collect data. We compared the result of traditional single-fiber acquisition with our proposed dual-fibers acquisition. For video reconstruction, we use a dual-scale matrix as the image measurement matrix which can restore images with two different spatial resolutions as needed. We use the low-resolution video as a preview to acquire optical flow, and then we reconstruct a better-quality video by using the optical flow as a restrictive condition. We built an actual single-pixel camera hardware platform based on dual-fiber acquisition, and we show that our high-quality video can be restored by collecting data from our single-pixel camera.
KeywordsSingle-pixel camera Dual-fiber acquisition Dual-scale matrix Optical flow Low frame rate
The authors sincerely thanks to the financial support from National Natural Science Foundation of China (61371102, 61001092) and in part by the Shenzhen Municipal Science and Technology Plan under Grant JCYJ20170811160142808, JCYJ20170811154309920.
- 3.D. Takhar, J. N. Laska, M. B. Wakin, et al. "A new compressive imaging camera architecture using optical-domain compression." Computational Imaging IV. Vol. 6065. International Society for Optics and Photonics, 2006.Google Scholar
- 6.M. Wakin, J. Laska, M. F. Duarte, et al. "Compressive imaging for video representation and coding." Picture Coding Symposium. Vol. 1. 2006.Google Scholar
- 7.S. Mun, and J. E. Fowler. "Residual reconstruction for block-based compressed sensing of video." Data Compression Conference (DCC), 2011. IEEE, 2011.Google Scholar
- 8.J. E. Fowler, S. Mun, and E. W. Tramel. "Block-based compressed sensing of images and video." Foundations and Trends? in Signal Processing 4.4 (2012): 297-416.Google Scholar
- 11.J. Kim, L. J. Kwon, and L. K. Mu. "Deeply-recursive convolutional network for image super-resolution." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.Google Scholar
- 12.Li C. An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing. Diss: Rice University; 2010.Google Scholar