Open AccessImage super‐resolution based on the pairwise dictionary selected learning and improved bilateral regularisation
Author(s) -
Gou Shuiping,
Liu Shuzhen,
Wu Yaosheng,
Jiao Licheng
Publication year - 2016
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2015.0046
Subject(s) - pairwise comparison , artificial intelligence , computer science , dictionary learning , image resolution , pattern recognition (psychology) , image (mathematics) , enhanced data rates for gsm evolution , resolution (logic) , noise (video) , computer vision , iterative reconstruction , k svd , superresolution
A pairwise dictionary selected learning (PDSL) model is proposed in this study, which is specially tailored to synthesise a low‐resolution image. This is accomplished with the use of an external dictionary of high‐resolution images, which is selectively learned based on the internal dictionary of the target reconstruction image. The PDSL can avoid interpolating the fictitious information to the reconstructed image. Optimisation is performed using a bilateral regularisation term in an edge reserving based on the aid of spatial and directional proximity. Using the proposed approach, the results achieved are superior to the existing dictionary learning‐based methods. The authors also present a quantitative evaluation of super‐resolution reconstruction using various statistics which demonstrated significant average peak signal‐to‐noise ratio improvements by their model. A comparison of the proposed method with five other state‐of‐the‐art methods is presented and the authors’ method achieves better visual effects in edge structures.