Dark channel inspired deblurring method for remote sensing image

Shixiang Cao; Wei Tan; Kun Xing; Hongyan He; Jie Jiang

doi:10.1117/1.JRS.12.015012

20 February 2018 Dark channel inspired deblurring method for remote sensing image

Shixiang Cao, Wei Tan, Kun Xing, Hongyan He, Jie Jiang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015012 (February 2018). https://doi.org/10.1117/1.JRS.12.015012

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.

Citation Download Citation

Shixiang Cao, Wei Tan, Kun Xing, Hongyan He, and Jie Jiang "Dark channel inspired deblurring method for remote sensing image," Journal of Applied Remote Sensing 12(1), 015012 (20 February 2018). https://doi.org/10.1117/1.JRS.12.015012

Received: 21 September 2017; Accepted: 23 January 2018; Published: 20 February 2018

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