Abstract
Compressed sensing is one of the most effective signal processing methods through the sparse representation of inferred data, in which dictionary matrices play an essential role and they are learned by feature extraction methods such as K-SVD ones. Therefore, in general, it requires a considerable amount of computational cost to construct a dictionary matrix. In this paper, we analytically derive the expression of the probability distribution followed by an image dictionary for compressed sensing, assuming that grey scale images are generated by the Gaussian model. This result enables us to directly generate a dictionary matrix for images with no edge, and can be the first step to analytical performance evaluation of image processing by compressed sensing.
| Original language | English |
|---|---|
| Title of host publication | ICCAS 2016 - 2016 16th International Conference on Control, Automation and Systems, Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 1377-1380 |
| Number of pages | 4 |
| ISBN (Electronic) | 9788993215120 |
| DOIs | |
| Publication status | Published - Jan 24 2017 |
| Event | 16th International Conference on Control, Automation and Systems, ICCAS 2016 - Gyeongju, Korea, Republic of Duration: Oct 16 2016 → Oct 19 2016 |
Other
| Other | 16th International Conference on Control, Automation and Systems, ICCAS 2016 |
|---|---|
| Country | Korea, Republic of |
| City | Gyeongju |
| Period | 10/16/16 → 10/19/16 |
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Keywords
- compressed sensing
- dictionary matrix
- ratio distribution
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Probability distribution of an image dictionary for compressed sensing. / Ashida, Yuhei; Aida, Toshiaki.
ICCAS 2016 - 2016 16th International Conference on Control, Automation and Systems, Proceedings. IEEE Computer Society, 2017. p. 1377-1380 7832490.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Probability distribution of an image dictionary for compressed sensing
AU - Ashida, Yuhei
AU - Aida, Toshiaki
PY - 2017/1/24
Y1 - 2017/1/24
N2 - Compressed sensing is one of the most effective signal processing methods through the sparse representation of inferred data, in which dictionary matrices play an essential role and they are learned by feature extraction methods such as K-SVD ones. Therefore, in general, it requires a considerable amount of computational cost to construct a dictionary matrix. In this paper, we analytically derive the expression of the probability distribution followed by an image dictionary for compressed sensing, assuming that grey scale images are generated by the Gaussian model. This result enables us to directly generate a dictionary matrix for images with no edge, and can be the first step to analytical performance evaluation of image processing by compressed sensing.
AB - Compressed sensing is one of the most effective signal processing methods through the sparse representation of inferred data, in which dictionary matrices play an essential role and they are learned by feature extraction methods such as K-SVD ones. Therefore, in general, it requires a considerable amount of computational cost to construct a dictionary matrix. In this paper, we analytically derive the expression of the probability distribution followed by an image dictionary for compressed sensing, assuming that grey scale images are generated by the Gaussian model. This result enables us to directly generate a dictionary matrix for images with no edge, and can be the first step to analytical performance evaluation of image processing by compressed sensing.
KW - compressed sensing
KW - dictionary matrix
KW - ratio distribution
UR - http://www.scopus.com/inward/record.url?scp=85014040285&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014040285&partnerID=8YFLogxK
U2 - 10.1109/ICCAS.2016.7832490
DO - 10.1109/ICCAS.2016.7832490
M3 - Conference contribution
AN - SCOPUS:85014040285
SP - 1377
EP - 1380
BT - ICCAS 2016 - 2016 16th International Conference on Control, Automation and Systems, Proceedings
PB - IEEE Computer Society
ER -