Abstract
Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures.
Keywords
Cite this article
@article{cai-4526,
author = {Xiuxiu Liao and Kejia Bai and Qian Zhang and Xiping Jia and Shaopeng Liu and Jin Zhan},
title = {Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries},
journal = {Computing and Informatics},
volume = {38},
number = {6},
pages = {1301--1319},
year = {2019},
issn = {1335-9150},
doi = {10.31577/cai_2019_6_1301}
}
TY - JOUR AU - Xiuxiu Liao AU - Kejia Bai AU - Qian Zhang AU - Xiping Jia AU - Shaopeng Liu AU - Jin Zhan TI - Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries JO - Computing and Informatics VL - 38 IS - 6 SP - 1301 EP - 1319 PY - 2019 SN - 1335-9150 DO - 10.31577/cai_2019_6_1301 UR - https://doi.org/10.31577/cai_2019_6_1301 KW - Image patch classification KW - multi-class dictionaries KW - phase congruency KW - sparse coding KW - super-resolution ER -
Liao, X., Bai, K., Zhang, Q., Jia, X., Liu, S., & Zhan, J. (2019). Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries. Computing and Informatics, 38(6), 1301–1319. https://doi.org/10.31577/cai_2019_6_1301