### 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 Sep 2 |

### Other

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

City | Budapest |

Period | 16/8/28 → 16/9/2 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Signal Processing
- Electrical and Electronic Engineering

### Cite this

*2016 24th European Signal Processing Conference, EUSIPCO 2016*(Vol. 2016-November, pp. 948-952). [7760388] European Signal Processing Conference, EUSIPCO. https://doi.org/10.1109/EUSIPCO.2016.7760388

**Sequential image completion for high-speed large-pixel number sensing.** / Hirabayashi, Akira; Nogami, Naoki; Ijiri, Takashi; Condat, Laurent.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2016 24th European Signal Processing Conference, EUSIPCO 2016.*vol. 2016-November, 7760388, European Signal Processing Conference, EUSIPCO, pp. 948-952, 24th European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary, 16/8/28. https://doi.org/10.1109/EUSIPCO.2016.7760388

}

TY - GEN

T1 - Sequential image completion for high-speed large-pixel number sensing

AU - Hirabayashi, Akira

AU - Nogami, Naoki

AU - Ijiri, Takashi

AU - Condat, Laurent

PY - 2016/11/28

Y1 - 2016/11/28

N2 - 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.

AB - 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.

KW - Compressed sensing

KW - Convex optimization

KW - High-speed camera

KW - Image completion

KW - Sparsity

UR - http://www.scopus.com/inward/record.url?scp=85006041172&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006041172&partnerID=8YFLogxK

U2 - 10.1109/EUSIPCO.2016.7760388

DO - 10.1109/EUSIPCO.2016.7760388

M3 - Conference contribution

VL - 2016-November

SP - 948

EP - 952

BT - 2016 24th European Signal Processing Conference, EUSIPCO 2016

PB - European Signal Processing Conference, EUSIPCO

ER -