Single Image Superresolution Using Maximizing Self-Similarity Prior
Joint Authors
Li, Jianhong
Wu, Yarong
Luo, Xiaonan
Leng, Chengcai
Li, Bo
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-08
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Single image superresolution (SISR) requires only one low-resolution (LR) image as its input which thus strongly motivates researchers to improve the technology.
The property that small image patches tend to recur themselves across different scales is very important and widely used in image processing and computer vision community.
In this paper, we develop a new approach for solving the problem of SISR by generalizing this property.
The main idea of our approach takes advantage of a generic prior that assumes that a randomly selected patch in the underlying high-resolution (HR) image should visually resemble as much as possible with some patch extracted from the input low-resolution (LR) image.
Observing the proposed prior, our approach deploys a cost function and applies an iterative scheme to estimate the optimal HR image.
For solving the cost function, we introduce Gaussian mixture model (GMM) to train on a sampled data set for approximating the joint probability density function (PDF) of input image with different scales.
Through extensive comparative experiments, this paper demonstrates that the visual fidelity of our proposed method is often superior to those generated by other state-of-the-art algorithms as determined through both perceptual judgment and quantitative measures.
American Psychological Association (APA)
Li, Jianhong& Wu, Yarong& Luo, Xiaonan& Leng, Chengcai& Li, Bo. 2016. Single Image Superresolution Using Maximizing Self-Similarity Prior. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1073490
Modern Language Association (MLA)
Li, Jianhong…[et al.]. Single Image Superresolution Using Maximizing Self-Similarity Prior. Mathematical Problems in Engineering No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1073490
American Medical Association (AMA)
Li, Jianhong& Wu, Yarong& Luo, Xiaonan& Leng, Chengcai& Li, Bo. Single Image Superresolution Using Maximizing Self-Similarity Prior. Mathematical Problems in Engineering. 2016. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1073490
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073490