A super-resolution algorithm using linear regression based on image self-similarity

Shen Chuan Tai, Jiun Jie Huang, Peng Yu Chen

研究成果: Conference contribution

摘要

The application of image super-resolution technologies in recent years has increased noticeably. The main purpose of image up-scaling is to obtain high-resolution images from low-resolution images, and these up-scaled images should keep satisfactory visual qualities and present natural textures. The most popular image up-scaling algorithms are based on interpolation methods in spatial domain. However, the up-scaled images may produce blurring artifacts. Therefore, using spatial sharpening filters is usually used to make blurred images sharp and clear. The quantity of image sharpening is the key to decide the visual qualities of up-scaled images. In this paper, a method based on self-similarity of images and using simple linear regression to build a reconstruction model for improving visual qualities of up-scaled images adaptively is proposed. The experimental results show that our algorithm provides better subjective visual qualities as well as the peak signal-to-noise ratio (PSNR).

原文English
主出版物標題Proceedings - 2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面275-278
頁數4
ISBN(電子)9781509030712
DOIs
出版狀態Published - 2016 八月 16
事件2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016 - Xi'an, China
持續時間: 2016 七月 42016 七月 6

出版系列

名字Proceedings - 2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016

Other

Other2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016
國家China
城市Xi'an
期間16-07-0416-07-06

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Control and Optimization

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