TR2010-029

Optimal Coded Sampling for Temporal Super-Resolution


    •  Agrawal, A.K., Gupta, M., Veeraraghavan, A.N., Narasimhan, S.G., "Optimal Coded Sampling for Temporal Super-Resolution", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/CVPR.2010.5540161, June 2010, pp. 599-606.
      BibTeX TR2010-029 PDF
      • @inproceedings{Agrawal2010jun,
      • author = {Agrawal, A.K. and Gupta, M. and Veeraraghavan, A.N. and Narasimhan, S.G.},
      • title = {Optimal Coded Sampling for Temporal Super-Resolution},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2010,
      • pages = {599--606},
      • month = jun,
      • doi = {10.1109/CVPR.2010.5540161},
      • url = {https://www.merl.com/publications/TR2010-029}
      • }
  • Research Area:

    Computer Vision

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 super-resolution 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 version cameras. We also propose a new class of invertible codes that allow continuous blur in captured frames, leading to an easier hardware implementation.

 

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    •  NEWS   CVPR 2010: 8 publications by C. Oncel Tuzel, Tim K. Marks, Yuichi Taguchi, Srikumar Ramalingam, Michael J. Jones and Amit K. Agrawal
      Date: June 13, 2010
      Where: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
      MERL Contacts: Michael Jones; Tim Marks
      Brief
      • The papers "Optimal Coded Sampling for Temporal Super-Resolution" by Agrawal, A.K., Gupta, M., Veeraraghavan, A.N. and Narasimhan, S.G., "Breaking the Interactive Bottleneck in Multi-class Classification with Active Selection and Binary Feedback" by Joshi, A.J., Porikli, F.M. and Papanikolopoulos, N., "Axial Light Field for Curved Mirrors: Reflect Your Perspective, Widen Your View" by Taguchi, Y., Agrawal, A.K., Ramalingam, S. and Veeraraghavan, A.N., "Morphable Reflectance Fields for Enhancing Face Recognition" by Kumar, R., Jones, M.J. and Marks, T.K., "Increasing Depth Resolution of Electron Microscopy of Neural Circuits using Sparse Tomographic Reconstruction" by Veeraraghavan, A., Genkin, A.V., Vitaladevuni, S., Scheffer, L., Xu, S., Hess, H., Fetter, R., Cantoni, M., Knott, G. and Chklovskii, D., "Specular Surface Reconstruction from Sparse Reflection Correspondences" by Sankaranarayanan, A., Veeraraghavan, A.N., Tuzel, C.O. and Agrawal, A.K., "Fast Directional Chamfer Matching" by Liu, M.-Y., Tuzel, C.O., Veeraraghavan, A.N. and Chellappa, R. and "Robust RVM regression using sparse outlier model" by Mitra, K., Veeraraghavan, A. and Chellappa, R. were presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
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