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Open AccessArticle

Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double 1 Constraint

by Songze Tang 1,*, Yang Xu 2, Lili Huang 3,4 and Le Sun 5
1
Department of Criminal Science and Technology, Nanjing Forest Police College, Nanjing 210023, China
2
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3
School of Science, Guangxi University of Science and Technology, Liuzhou 545006, China
4
School of Computer Science and Technology, Jinling Institute of Technology, Nanjing 211169, China
5
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2809; https://doi.org/10.3390/rs11232809
Received: 24 October 2019 / Revised: 24 November 2019 / Accepted: 25 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue Advances in Remote Sensing Image Fusion)
Hyperspectral image (HSI) super-resolution (SR) is an important technique for improving the spatial resolution of HSI. Recently, a method based on sparse representation improved the performance of HSI SR significantly. However, the spectral dictionary was learned under a fixed size, empirically, without considering the training data. Moreover, most of the existing methods fail to explore the relationship among the sparse coefficients. To address these crucial issues, an effective method for HSI SR is proposed in this paper. First, a spectral dictionary is learned, which can adaptively estimate a suitable size according to the input HSI without any prior information. Then, the proposed method exploits the nonlocal correlation of the sparse coefficients. Double 1 regularized sparse representation is then introduced to achieve better reconstructions for HSI SR. Finally, a high spatial resolution HSI is generated by the obtained coefficients matrix and the learned adaptive size spectral dictionary. To evaluate the performance of the proposed method, we conduct experiments on two famous datasets. The experimental results demonstrate that it can outperform some relatively state-of-the-art methods in terms of the popular universal quality evaluation indexes. View Full-Text
Keywords: hyperspectral image super-resolution; sparse representation; adaptive dictionary learning; double 1 hyperspectral image super-resolution; sparse representation; adaptive dictionary learning; double 1
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MDPI and ACS Style

Tang, S.; Xu, Y.; Huang, L.; Sun, L. Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double 1 Constraint. Remote Sens. 2019, 11, 2809.

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