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Proceedings Paper

Image super-resolution based on self-similarity and various patch size
Author(s): Luyang Yao; Tong Li; Yushan Ye; Kai Xie; Shiwei Zan
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Paper Abstract

A new single image super-resolution method based on self-similarity across different scales and pyramid model is proposed. In order to enrich the diversity of the training patches but not increase the computational complexity, we rotate the low resolution input image by a certain angle from 0° to 90° and down-sample them into 2 layers pyramid model respectively. However, most self-similarity super-resolution algorithms was carried out by the fixed size of patch. So, in this paper we observe the effect of patch size using the various patch size then pick out the most appropriate patch size. During the mapping process, we use the Fast Library for Approximate Nearest Neighbors (FLANN) to search the corresponding nine closest patches in high-frequency pyramid then carry out Gaussian weighted (SSD), which can avoid the occasionality and mismatch by using the nearest neighbor strategy. Finally, the local constraint and the iterative back projection algorithm are adopted to optimize the reconstructed image. Experimental results validate that the algorithm is better than the traditional algorithm in visual effects and computational complexity.

Paper Details

Date Published: 29 August 2016
PDF: 7 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334F (29 August 2016); doi: 10.1117/12.2243771
Show Author Affiliations
Luyang Yao, Beijing Institute of Graphic Communication (China)
Tong Li, Beijing Institute of Graphic Communication (China)
Yushan Ye, Beijing Institute of Graphic Communication (China)
Kai Xie, Beijing Institute of Graphic Communication (China)
Shiwei Zan, Beijing Institute of Graphic Communication (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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