Optimized Quality and Structure Using Adaptive Total Variation and MM Algorithm for Single Image Super-Resolution

  Shanta Patel,  Sanket Choudhary

In this work, proposed an approach, which explores both structural and statistical information of image patches to learn multiple dictionaries for super-resolving an image in sparse domain. In this paper propose a novel computationally efficient single image SR method that learns multiple linear mappings to directly transform LR feature subspaces into HR subspaces. Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing results without extensive training on external databases. Such algorithms exploit the statistical prior that patches in a natural image tend to recur within and across scales of the same image. Structural information is estimated using dominant edge orientation, and means value of the intensity levels of an image patch is used to represent statistical information

Keywords- Sparse representation, Dictionary, Edge orientation, Clustering, Edge preserving constraint, Super-resolution

Unique Identification Number - IJEDR1704035
Page Number(s) - 231-236
Pubished in - Volume 5 | Issue 4 | October 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Shanta Patel,  Sanket Choudhary,   "Optimized Quality and Structure Using Adaptive Total Variation and MM Algorithm for Single Image Super-Resolution", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 4, pp.231-236, October 2017, Available at :http://www.ijedr.org/papers/IJEDR1704035.pdf