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Proceedings Paper

Single image super resolution based on multi-scale structural self similarity and neighborhood regression
Author(s): Ziwei Lu; Chengdong Wu; Xiaosheng Yu; Chen Hong
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Paper Abstract

Multi-scale structural self-similarity refer to those similar structures recurring many times within and across scales of the same image. In this paper, we present a single image super resolution (SR) method based on multi-scale structural selfsimilarity and neighborhood regression, which reconstructs a high resolution (HR) image from the image pyramid of the input image itself without depending on extrinsic set of training images. In the proposed approach, we find the nearest neighbor patches for each low resolution (LR) image patch, and then learn the neighborhood regression to map low resolution space to high resolution space. Experimental results show that our approach acquires better result in peak signal to noise ratio and visual effects against several competing methods.

Paper Details

Date Published: 9 August 2018
PDF: 5 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080633 (9 August 2018); doi: 10.1117/12.2502973
Show Author Affiliations
Ziwei Lu, Northeastern Univ. (China)
Liaoning Shihua Univ. (China)
Chengdong Wu, Northeastern Univ. (China)
Xiaosheng Yu, Northeastern Univ. (China)
Chen Hong, Northeastern Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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