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Proceedings Paper

Image super-resolution via multistage sparse coding
Author(s): Min Shi; Qingming Yi; Xin Yang
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Paper Abstract

To reduce the reconstruction error in dictionary training and reconstruction, an image super-resolution algorithm via multistage sparse coding (SMSC) is proposed in this paper. The combined Lanczos3 and IBP algorithm is used as the first method to estimate the high resolution image. In dictionary training, the feature and reconstruction error of estimated images are used to train multistage feature dictionaries and error dictionaries. In reconstruction, using feature dictionaries and error dictionaries, the error term of the estimated image is reconstructed by sparse coding to improve the image quality stage by stage. The experiment shows that, the proposed algorithm outperforms other the-state-of-art SR algorithm SISR in image quality, while the reconstruction time remains in low level.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100334A (29 August 2016); doi: 10.1117/12.2244850
Show Author Affiliations
Min Shi, Jinan Univ. (China)
Qingming Yi, Jinan Univ. (China)
Xin Yang, Actions (Zhuhai) Technology Co., Ltd. (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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