Image interpolation based on weighting function of Gaussian

Takuro Yamaguchi, Masaaki Ikehara, Yasuhiro Nakajima

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages1193-1197
Number of pages5
Volume2016-February
ISBN (Print)9781467385763
DOIs
Publication statusPublished - 2016 Feb 26
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: 2015 Nov 82015 Nov 11

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period15/11/815/11/11

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

Cite this

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, pp. 1193-1197). [7421329] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421329