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Sensors 2017, 17(2), 362; doi:10.3390/s17020362

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

1,2
,
1,* , 3
and
1,*
1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
SiChuan Remote Sensing Geomatics Institute, NO. 2, Jianshe Road, Longquanyi District, Chengdu 610100, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Huajun Tang, Wenbin Wu and Yun Shi
Received: 5 December 2016 / Revised: 3 February 2017 / Accepted: 4 February 2017 / Published: 13 February 2017
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
View Full-Text   |   Download PDF [11729 KB, uploaded 16 February 2017]   |  

Abstract

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the highresolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. View Full-Text
Keywords: super resolution; point spread function (PSF); projections onto convex sets (POCS); remote sensing. super resolution; point spread function (PSF); projections onto convex sets (POCS); remote sensing.
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Fan, C.; Wu, C.; Li, G.; Ma, J. Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images. Sensors 2017, 17, 362.

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