Optimal coded sampling for temporal super-resolution

• Published in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 599 - 606
• Main Authors: Agrawal, A, Gupta, M, Veeraraghavan, A, Narasimhan, S G
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Additive noise; Cameras; Gaussian noise; Hardware; Image reconstruction; Image sampling; Linear systems; Machine vision; Sampling methods; Signal to noise ratio
• ISBN: 1424469848; 9781424469840
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2010.5540161

Conventional low frame rate cameras result in blur and/or aliasing in images while capturing fast dynamic events. Multiple low speed cameras have been used previously with staggered sampling to increase the temporal resolution. However, previous approaches are inefficient: they either use small integration time for each camera which does not provide light benefit, or use large integration time in a way that requires solving a big ill-posed linear system. We propose coded sampling that address these issues: using N cameras it allows N times temporal superresolution while allowing ~N/2 times more light compared to an equivalent high speed camera. In addition, it results in a well-posed linear system which can be solved independently for each frame, avoiding reconstruction artifacts and significantly reducing the computational time and memory. Our proposed sampling uses optimal multiplexing code considering additive Gaussian noise to achieve the maximum possible SNR in the recovered video. We show how to implement coded sampling on off-the-shelf machine vision cameras. We also propose a new class of invertible codes that allow continuous blur in captured frames, leading to an easier hardware implementation.