NDLI: Single Image Super-Resolution Based on Local Self-Similarity

Content Provider	IEEE Xplore Digital Library
Author	Wun-Ting Lin Shang-Hong Lai
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan (Wun-Ting Lin; Shang-Hong Lai)
Abstract	Single image super-resolution, namely increasing the resolution from only one coarse-resolution image, is a fundamental problem in computer vision. Although it has been extensively studied for decades, super-resolving a real-world image still remains challenging. In this paper, we propose a novel approach for image super-resolution by exploiting local self-similarity. First, we take advantage of this property by binding several similar patches found in a limited window into a group. Then, a novel super resolution technique is applied in the patch-based manner along with the classical reconstruction-based framework by replacing the required multiple inputs with the aforementioned group, which consists of numerous similar patches and holds the vital registration information required in the super-resolution. Experimental results demonstrate the high quality of proposed algorithm through objective DIIVINE index score as well as the subjective user study evaluation. Both ways of evaluation strongly support that the proposed single-image super-resolution algorithm is competitive and provides satisfactory results.
Starting Page	191
Ending Page	195
File Size	649937
Page Count	5
File Format	PDF
ISBN	9781479921904
DOI	10.1109/ACPR.2013.107
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-05
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Single-image super-resolution Local self-similarity Image edge detection Mathematical model Kernel Spatial resolution Reconstruction-based super-resolution Image reconstruction Equations
Content Type	Text
Resource Type	Article

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