Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
Non-causal temporal prior for video deblocking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
On Plenoptic Multiplexing and Reconstruction
International Journal of Computer Vision
Compressive light field photography using overcomplete dictionaries and optimized projections
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Adaptive sampling for low latency vision processing
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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We describe an imaging architecture for compressive video sensing termed programmable pixel compressive camera (P2C2). P2C2 allows us to capture fast phenomena at frame rates higher than the camera sensor. In P2C2, each pixel has an independent shutter that is modulated at a rate higher than the camera frame-rate. The observed intensity at a pixel is an integration of the incoming light modulated by its specific shutter. We propose a reconstruction algorithm that uses the data from P2C2 along with additional priors about videos to perform temporal super-resolution. We model the spatial redundancy of videos using sparse representations and the temporal redundancy using brightness constancy constraints inferred via optical flow. We show that by modeling such spatio-temporal redundancies in a video volume, one can faithfully recover the underlying high-speed video frames from the observed low speed coded video. The imaging architecture and the reconstruction algorithm allows us to achieve temporal super-resolution without loss in spatial resolution. We implement a prototype of P2C2 using an LCOS modulator and recover several videos at 200 fps using a 25 fps camera.