Image interpolation based on weighting function of Gaussian

Takuro Yamaguchi; Masaaki Ikehara; Yasuhiro Nakajima

doi:10.1109/ACSSC.2015.7421329

Image interpolation based on weighting function of Gaussian

Takuro Yamaguchi, Masaaki Ikehara, Yasuhiro Nakajima

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

1 Citation (Scopus)

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	https://doi.org/10.1109/ACSSC.2015.7421329
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

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

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Yamaguchi, T, Ikehara, M & Nakajima, Y 2016, Image interpolation based on weighting function of Gaussian. in Conference Record - Asilomar Conference on Signals, Systems and Computers. vol. 2016-February, 7421329, IEEE Computer Society, pp. 1193-1197, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 15/11/8. https://doi.org/10.1109/ACSSC.2015.7421329

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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.",

author = "Takuro Yamaguchi and Masaaki Ikehara and Yasuhiro Nakajima",

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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.

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