For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Exploration into Single Image Super-Resolution via Self Similarity by Sparse Representation
Lv GUO Yin LI Jie YANG Li LU
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2010/11/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
super resolution, self similarity, sparse coding,
Full Text: PDF>>
A novel method for single image super resolution without any training samples is presented in the paper. By sparse representation, the method attempts to recover at each pixel its best possible resolution increase based on the self similarity of the image patches across different scale and rotation transforms. The experiments indicate that the proposed method can produce robust and competitive results.