Spatio-temporal sampling for video

With this work we propose spatio-temporal sampling strategies for video using a lenslet array computational imaging system and explore the opportunities and challenges in the design of compressive video sensors and corresponding processing algorithms. The redundancies in video streams are exploited by (a) sampling the sub-apertures of a multichannel (TOMBO) camera, and (b) by the computational reconstruction to achieve low power and low complexity video sensors. A spatial and a spatio-temporal sampling strategy are considered, taking into account the feasibility for implementation in the focal-plane readout hardware. The algorithms used to reconstruct the video frames from measurements are also presented.

[1]  Li Hong.  Superresolution video reconstruction , 2003, IS&T/SPIE Electronic Imaging.

[2]  A. Murat Tekalp,et al.  High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[4]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[5]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[6]  Caihua Chen,et al.  Thin infrared imaging systems through multichannel sampling. , 2008, Applied optics.

[7]  J. Tanida,et al.  Thin Observation Module by Bound Optics (TOMBO): Concept and Experimental Verification. , 2001, Applied optics.

[8]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .

[9]  Richard G. Baraniuk,et al.  An Architecture for Compressive Imaging , 2006, 2006 International Conference on Image Processing.