NDLI: Image super-resolution based on adaptive cosparse regularisation

Content Provider	IET Digital Library
Author	Chen, Huahua Xue, Jiling Zhang, Song Lu, Yu Guo, Chunsheng
Abstract	A novel regularised image super-resolution algorithm is proposed, building on the emerging cosparse or analysis sparse prior models, which are important complementary alternatives to the widely used synthesis sparse counterpart. Moreover, to achieve adaptivity to the varying local structures of natural images, the patch space is partitioned into meaningful subspaces by clustering and learn analysis sub-dictionary for each cluster are partitioned, which are performed online and iteratively based solely on the current available image information, for maximum generality and flexibility. In addition, non-local feature self-similarity is incorporated for further reconstruction quality enhancement. Experimental results show that the proposed approach gives favourable results with respect to the state-of-the-art methods.
Starting Page	1834
Ending Page	1836
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 24, Nov (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/24
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.1429
Journal	Electronics Letters
Publisher Date	2014-11-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Cosparse Regularisation Analysis Subdictionary Computer Vision And Image Processing Technique Cosparse Prior Model Image Enhancement Image Information Image Reconstruction Image Resolution Local Structure Maximum Generality Natural Image Nonlocal Feature Self-similarity Optical, Image And Video Signal Processing Reconstruction Quality Enhancement Regularised Image Super-resolution Algorithm Sparse Prior Model Synthesis Sparse Counterpart
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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