Abstract
In this paper, we propose a new image interpolation method based on a 2-D piecewise stationary autoregressive (PAR) model. SAI, which defined PAR model, is a state-of-the-art method in image interpolation. It produces good quality restored images but has high calculation cost because of solving multiple least-square problems. Our method utilizes Gaussian function in estimating parameters instead of solving least-square problems and reduces the calculation cost. Moreover, parameters are estimated at each pixel, while they are estimated in each local window in SAI. By these improvements, the proposed method has equivalent quality to SAI with low calculation cost.
| Original language | English |
|---|---|
| Title of host publication | Conference Record - Asilomar Conference on Signals, Systems and Computers |
| Publisher | IEEE Computer Society |
| Pages | 1193-1197 |
| Number of pages | 5 |
| Volume | 2016-February |
| ISBN (Print) | 9781467385763 |
| DOIs | |
| Publication status | Published - 2016 Feb 26 |
| Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: 2015 Nov 8 → 2015 Nov 11 |
Other
| Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
|---|---|
| Country | United States |
| City | Pacific Grove |
| Period | 15/11/8 → 15/11/11 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing
Cite this
Image interpolation based on weighting function of Gaussian. / Yamaguchi, Takuro; Ikehara, Masaaki; Nakajima, Yasuhiro.
Conference Record - Asilomar Conference on Signals, Systems and Computers. Vol. 2016-February IEEE Computer Society, 2016. p. 1193-1197 7421329.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Image interpolation based on weighting function of Gaussian
AU - Yamaguchi, Takuro
AU - Ikehara, Masaaki
AU - Nakajima, Yasuhiro
PY - 2016/2/26
Y1 - 2016/2/26
N2 - In this paper, we propose a new image interpolation method based on a 2-D piecewise stationary autoregressive (PAR) model. SAI, which defined PAR model, is a state-of-the-art method in image interpolation. It produces good quality restored images but has high calculation cost because of solving multiple least-square problems. Our method utilizes Gaussian function in estimating parameters instead of solving least-square problems and reduces the calculation cost. Moreover, parameters are estimated at each pixel, while they are estimated in each local window in SAI. By these improvements, the proposed method has equivalent quality to SAI with low calculation cost.
AB - In this paper, we propose a new image interpolation method based on a 2-D piecewise stationary autoregressive (PAR) model. SAI, which defined PAR model, is a state-of-the-art method in image interpolation. It produces good quality restored images but has high calculation cost because of solving multiple least-square problems. Our method utilizes Gaussian function in estimating parameters instead of solving least-square problems and reduces the calculation cost. Moreover, parameters are estimated at each pixel, while they are estimated in each local window in SAI. By these improvements, the proposed method has equivalent quality to SAI with low calculation cost.
UR - http://www.scopus.com/inward/record.url?scp=84969820276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969820276&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2015.7421329
DO - 10.1109/ACSSC.2015.7421329
M3 - Conference contribution
AN - SCOPUS:84969820276
SN - 9781467385763
VL - 2016-February
SP - 1193
EP - 1197
BT - Conference Record - Asilomar Conference on Signals, Systems and Computers
PB - IEEE Computer Society
ER -