NDLI: Adaptive regularised l 2-boosting on clustered sparse coefficients for single image super-resolution

Content Provider	IET Digital Library
Author	Han, Yulan Zhao, Yongping Yu, Haifeng
Abstract	In this study, the authors propose a novel approach for single image super-resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low-resolution (LR) and high-resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l 2-boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l 2-boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.
Starting Page	517
Ending Page	529
Page Count	13
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 7, Oct (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0274
Journal	IET Computer Vision
Publisher Date	2017-01-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Regularised L2-boosting Algorithm Clustered Sparse Coefficient Computer Vision And Image Processing Technique High-resolution Image Patch HR Image Patch Image Reconstruction Image Resolution Knowledge Engineering Technique Learning in AI Low-resolution Image Patch LR Image Patch Mapping Function Learning Optical, Image And Video Signal Processing Pattern Clustering Single Image Super-resolution
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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