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Journal of Applied Remote Sensing

Dark channel inspired deblurring method for remote sensing image
Author(s): Shixiang Cao; Wei Tan; Kun Xing; Hongyan He; Jie Jiang
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Paper Abstract

In the remote sensing community, blur is a prevalent phenomenon especially for image using system parameter away from ideal truth. According to the relationship between dark channel and convolution, a modified and more applicable method is proposed here, which mainly contains blind kernel estimation and nonblind deconvolution. A reconstructed energy function, minimizing the sparsity and the value of dark channel, generates an accurate kernel; an effective module is introduced to preserve the texture and avoid artifacts; and finally a parallel framework is designed for large image. From the objective metrics on demo case, our approach is more effective to model and remove blurs than previous approaches, and furthermore we demonstrate its activity with experiments on real images.

Paper Details

Date Published: 20 February 2018
PDF: 13 pages
J. Appl. Rem. Sens. 12(1) 015012 doi: 10.1117/1.JRS.12.015012
Published in: Journal of Applied Remote Sensing Volume 12, Issue 1
Show Author Affiliations
Shixiang Cao, Beijing Institute of Space Mechanics and Electricity (China)
Wei Tan, Beijing Institute of Space Mechanics and Electricity (China)
Kun Xing, Beijing Institute of Space Mechanics and Electricity (China)
Hongyan He, Beijing Institute of Space Mechanics and Electricity (China)
Jie Jiang, Beihang Univ. (China)


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