Title
Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images.
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 high-resolution (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.
Year
DOI
Venue
2017
10.3390/s17020362
SENSORS
Keywords
Field
DocType
super resolution,point spread function (PSF),projections onto convex sets (POCS),remote sensing
Projections onto convex sets,Computer vision,Embedding,Digital sensors,Remote sensing,Interpolation,Bicubic interpolation,Convex set,Artificial intelligence,Engineering,Point spread function,Image resolution
Journal
Volume
Issue
ISSN
17
2.0
1424-8220
Citations 
PageRank 
References 
4
0.41
9
Authors
4
Name
Order
Citations
PageRank
FAN Chong1232.78
Chaoyun Wu240.41
Grand Li340.41
Jun Ma44719.80