NDLI: Locally regularized Anchored Neighborhood Regression for fast Super-Resolution

Content Provider	IEEE Xplore Digital Library
Author	Junjun Jiang Jican Fu Tao Lu Ruimin Hu Zhongyuan Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Printing & Packaging, Wuhan Univ., Wuhan, China (Jican Fu) \|\| Sch. of Comput. Sci., China Univ. of Geosci., Wuhan, China (Junjun Jiang) \|\| Hubei Province Key Lab. of Intell. Robot, WIT, Wuhan, China (Tao Lu) \|\| Sch. of Comput., Wuhan Univ., Wuhan, China (Ruimin Hu; Zhongyuan Wang)
Abstract	The goal of learning-based image Super-Resolution (SR) is to generate a plausible and visually pleasing High-Resolution (HR) image from a given Low-Resolution (LR) input. The problem is dramatically under-constrained, which relies on examples or some strong image priors to better reconstruct the missing HR image details. This paper addresses the problem of learning the mapping functions (i.e. projection matrices) between the LR and HR images based on a dictionary of LR and HR examples. One recently proposed method, Anchored Neighborhood Regression (ANR) [1], provides state-of-the-art quality performance and is very fast. In this paper, we propose an improved variant of ANR, namely Locally regularized Anchored Neighborhood Regression (LANR), which utilizes the locality-constrained regression in place of the ridge regression in ANR. LANR assigns different freedom for each neighbor dictionary atom according to its correlation to the input LR patch, thus the learned projection matrices are much more flexible. Experimental results demonstrate that the proposed algorithm performs efficiently and effectively over state-of-the-art methods, e.g., 0.1-0.4 dB in term of PSNR better than ANR.
Starting Page	1
Ending Page	6
File Size	5461032
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177470
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Image resolution Image reconstruction Encoding Face Correlation Feature extraction Locality Prior Super-Resolution Neighbor Embedding Sparse Coding Linear Regression
Content Type	Text
Resource Type	Article

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