NDLI: Hierarchical sparse representation with adaptive dictionaries for image super-resolution

Content Provider	IEEE Xplore Digital Library
Author	Xuelian Wu Daiguo Deng Jianhong Li Xiaonan Luo Kun Zeng
Copyright Year	2013
Description	Author affiliation: Sch. of Software, Sun Yat-sen Univ., Guangzhou, China (Kun Zeng) \|\| State-Province Joint Lab. of Digital Home Interactive Applic., Sun Yat-sen Univ., Shenzhen, China (Xuelian Wu; Daiguo Deng; Jianhong Li; Xiaonan Luo)
Abstract	This paper presents an image hierarchical super-resolution (SR) method with adaptive dictionaries, based on signal sparse representation. It can not only improve image detail quality but also reduce computational cost. Research on the human visual system suggests that our eyes are mainly sensitive to high-frequency contents. Inspired by this observation, we implemented a hierarchical process where an image was decomposed into a detail layer and a base layer. The detail layer is reconstructed through an over-complete dictionary while the base layer is interpolated by bi-cubic. Through these, we can keep the HR details better. Next is how to accelerate while keeping good quality. In our method, adaptive dictionaries are trained by feature clustering. Firstly, we train low dimension sub-dictionaries to reduce time complexity. Secondly, then we apply overlapping feature clustering to the training. Thus dictionaries can be adaptive and more complete. All these can also prevent sub-dictionaries with over strong independence but less compatibility. Besides, initializing the sparse coefficients also plays an important role in our acceleration. Experimental results validate that ours are competitive or even superior in quality than those produced by other methods and our test data indicates substantial reduction in processing time over other similar SR methods.
Sponsorship	IEEE Eng.Med. Biol. Soc.
Starting Page	272
Ending Page	276
File Size	989418
Page Count	5
File Format	PDF
e-ISBN	9781479927647
DOI	10.1109/CISP.2013.6744001
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Dictionaries Image resolution PSNR super-resolution Adaptive dictionary feature clustering Signal resolution Image reconstruction
Content Type	Text
Resource Type	Article

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