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Remote Sens. 2014, 6(8), 7491-7521; doi:10.3390/rs6087491

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

1
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
3
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 27 April 2014 / Revised: 24 July 2014 / Accepted: 5 August 2014 / Published: 13 August 2014
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Abstract

In this paper, a blind restoration method is presented to remove the blur in remote sensing images. An alternating minimization (AM) framework is employed to simultaneously recover the image and the point spread function (PSF), and an adaptive-norm prior is used to apply different constraints to smooth regions and edges. Moreover, with the use of the knife-edge features in remote sensing images, an automatic knife-edge detection method is used to obtain a good initial PSF for the AM framework. In addition, a no-reference (NR) sharpness index is used to stop the iterations of the AM framework automatically at the best visual quality. Results in both simulated and real data experiments indicate that the proposed AM-KEdge method, which combines the automatic knife-edge detection and the AM framework, is robust, converges quickly, and can stop automatically to obtain satisfactory results. View Full-Text
Keywords: blind restoration; knife edge; initial PSF; alternating minimization; automatic stopping criterion blind restoration; knife edge; initial PSF; alternating minimization; automatic stopping criterion
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Shen, H.; Zhao, W.; Yuan, Q.; Zhang, L. Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization. Remote Sens. 2014, 6, 7491-7521.

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