## Abstract

We propose an algorithm that enhances the number of pixels for high-speed camera imaging to suppress its main problem. That is, the number of pixels reduces when the number of frames per second (fps) increases. To this end, we suppose an optical setup that block-randomly selects some percent of pixels in an image. Then, the proposed algorithm reconstructs the entire image from the selected partial pixels. In this algorithm, two types of sparsity are exploited. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. Simulation results show that the proposed method outperforms the standard approach for image completion by the nuclear norm minimization.

Original language | English |
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Title of host publication | 2016 24th European Signal Processing Conference, EUSIPCO 2016 |

Publisher | European Signal Processing Conference, EUSIPCO |

Pages | 948-952 |

Number of pages | 5 |

Volume | 2016-November |

ISBN (Electronic) | 9780992862657 |

DOIs | |

Publication status | Published - 2016 Nov 28 |

Externally published | Yes |

Event | 24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary Duration: 2016 Aug 28 → 2016 Sept 2 |

### Other

Other | 24th European Signal Processing Conference, EUSIPCO 2016 |
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Country/Territory | Hungary |

City | Budapest |

Period | 16/8/28 → 16/9/2 |

## Keywords

- Compressed sensing
- Convex optimization
- High-speed camera
- Image completion
- Sparsity

## ASJC Scopus subject areas

- Signal Processing
- Electrical and Electronic Engineering