IEICE TRANSACTIONS on Fundamentals
Toward Large-Pixel Number High-Speed Imaging Exploiting Time and Space Sparsity
Summary :
In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. 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. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.6 pp.1279-1285
- Publication Date
- 2017/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.1279
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Naoki NOGAMI
Ritsumeikan University
Akira HIRABAYASHI
Ritsumeikan University
Takashi IJIRI
Ritsumeikan University
Jeremy WHITE
Ritsumeikan University
Keyword
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Naoki NOGAMI, Akira HIRABAYASHI, Takashi IJIRI, Jeremy WHITE, "Toward Large-Pixel Number High-Speed Imaging Exploiting Time and Space Sparsity" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 6, pp. 1279-1285, June 2017, doi: 10.1587/transfun.E100.A.1279.
Abstract: In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. 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. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1279/_p
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@ARTICLE{e100-a_6_1279,
author={Naoki NOGAMI, Akira HIRABAYASHI, Takashi IJIRI, Jeremy WHITE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Toward Large-Pixel Number High-Speed Imaging Exploiting Time and Space Sparsity},
year={2017},
volume={E100-A},
number={6},
pages={1279-1285},
abstract={In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. 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. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.},
keywords={},
doi={10.1587/transfun.E100.A.1279},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - Toward Large-Pixel Number High-Speed Imaging Exploiting Time and Space Sparsity
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1279
EP - 1285
AU - Naoki NOGAMI
AU - Akira HIRABAYASHI
AU - Takashi IJIRI
AU - Jeremy WHITE
PY - 2017
DO - 10.1587/transfun.E100.A.1279
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2017
AB - In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. 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. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.
ER -
English
