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Remote Sens. 2016, 8(6), 499; doi:10.3390/rs8060499

Reference Information Based Remote Sensing Image Reconstruction with Generalized Nonconvex Low-Rank Approximation

1
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
2
Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China
3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
4
School of Computer Science, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabail
Received: 4 March 2016 / Revised: 1 May 2016 / Accepted: 31 May 2016 / Published: 14 June 2016
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Abstract

Because of the contradiction between the spatial and temporal resolution of remote sensing images (RSI) and quality loss in the process of acquisition, it is of great significance to reconstruct RSI in remote sensing applications. Recent studies have demonstrated that reference image-based reconstruction methods have great potential for higher reconstruction performance, while lacking accuracy and quality of reconstruction. For this application, a new compressed sensing objective function incorporating a reference image as prior information is developed. We resort to the reference prior information inherent in interior and exterior data simultaneously to build a new generalized nonconvex low-rank approximation framework for RSI reconstruction. Specifically, the innovation of this paper consists of the following three respects: (1) we propose a nonconvex low-rank approximation for reconstructing RSI; (2) we inject reference prior information to overcome over smoothed edges and texture detail losses; (3) on this basis, we combine conjugate gradient algorithms and a single-value threshold (SVT) simultaneously to solve the proposed algorithm. The performance of the algorithm is evaluated both qualitatively and quantitatively. Experimental results demonstrate that the proposed algorithm improves several dBs in terms of peak signal to noise ratio (PSNR) and preserves image details significantly compared to most of the current approaches without reference images as priors. In addition, the generalized nonconvex low-rank approximation of our approach is naturally robust to noise, and therefore, the proposed algorithm can handle low resolution with noisy inputs in a more unified framework. View Full-Text
Keywords: remote sensing image reconstruction; low-rank regularization; nonconvex optimization; reference information remote sensing image reconstruction; low-rank regularization; nonconvex optimization; reference information
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, H.; Wei, J.; Wang, L.; Liu, P.; Liu, Q.; Wang, Y.; Deng, X. Reference Information Based Remote Sensing Image Reconstruction with Generalized Nonconvex Low-Rank Approximation. Remote Sens. 2016, 8, 499.

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