Single Image Super-Resolution from Local Self-examples Based on an Improved Similarity Measurement Model
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    The accurate matching of high and low resolution image blocks is the key of self-examples super resolution algorithm. In the process of blocks matching of high and low resolution images, considering the importance of texture image block structure, a similarity metric model based on constrained texture image patch is proposed in this paper. By using this exact matching model, the detail of super-resolution result image is further enriched, and the image quality is improved also. The new algorithm has the particular advantage of improving spatial resolution of image only using prior information of single low-resolution image itself. The experimental results show that the proposed algorithm has a better super-resolution visual effect compared with the bicubic interpolation algorithm and the local self-examples super-resolution algorithm, and it also has a good performance in the objective evaluation index.

    Reference
    Related
    Cited by
Get Citation

Zhao Liling, Sun Quansen. Single Image Super-Resolution from Local Self-examples Based on an Improved Similarity Measurement Model[J].,2018,33(2):240-247.

Copy
Related Videos

Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:September 06,2016
  • Revised:December 06,2016
  • Adopted:
  • Online: July 09,2018
  • Published:
Article QR Code