Paper Info
Title
Anchored Projection Based Capped L(2,1)-Norm Regression For Super-Resolution
Abstract
Single image super resolution task is aimed to recover a high resolution image with pleasing visual quality from a single low resolution image. It is a highly under-constrained problem because of the ambiguous mapping between low/high resolution patch domain. In order to alleviate the ambiguity problem, we split input patches into numerous subclasses and collect exemplars according to the sparse dictionary atoms. However, we observe that there still exist some similar regressors do not share the same regression in the same subclass, which may increase the super-resolving error for training data in each cluster. In this paper, we propose a robust and effective method based capped l(2,1)-norm regression to address this problem. The proposed method can automatically exclude outliers in each cluster during the training phase and give the potential to learn local prior information accurately. Numerous experimental results demonstrate that the proposed algorithm achieves better reconstruction performance against other state-of-the-art methods.
Year
DOI
Venue
2018
10.1007/978-3-319-97310-4_2
PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II
Keywords
Field
DocType
Capped l(2,1)-norm regression, Local linear regression, Single image super resolution
Training set,Pattern recognition,Regression,Effective method,Computer science,Outlier,Local regression,Artificial intelligence,Superresolution,Ambiguity
Conference
Volume
ISSN
Citations 
11013
0302-9743
0
PageRank 
References 
Authors
0.34
12
5
Name
Order
Citations
PageRank
Xiaotian Ma100.34
Mingbo Zhao212510.52
Zhao Zhang393865.99
Jicong Fan4819.62
Choujun Zhan5122.17