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Blind UAV Images Deblurring Based on Discriminative Networks

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2874;
Received: 22 June 2018 / Revised: 3 August 2018 / Accepted: 27 August 2018 / Published: 31 August 2018
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
Unmanned aerial vehicles (UAVs) have become an important technology for acquiring high-resolution remote sensing images. Because most space optical imaging systems of UAVs work in environments affected by vibrations, the optical axis motion and image plane jitter caused by these vibrations easily result in blurring of UAV images. In the paper; we propose an advanced UAV image deblurring method based on a discriminative model comprising a classifier for blurred and sharp UAV images which is embedded into the maximum a posteriori framework as a regularization term that constantly optimizes ill-posed problem of blind image deblurring to obtain sharper UAV images. Compared with other methods, the results show that in image deblurring experiments using both simulated and real UAV images the proposed method delivers sharper images of various ground objects. View Full-Text
Keywords: UAV images; image deblurring; image prior; discriminative networks UAV images; image deblurring; image prior; discriminative networks
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MDPI and ACS Style

Wang, R.; Ma, G.; Qin, Q.; Shi, Q.; Huang, J. Blind UAV Images Deblurring Based on Discriminative Networks. Sensors 2018, 18, 2874.

AMA Style

Wang R, Ma G, Qin Q, Shi Q, Huang J. Blind UAV Images Deblurring Based on Discriminative Networks. Sensors. 2018; 18(9):2874.

Chicago/Turabian Style

Wang, Ruihua, Guorui Ma, Qianqing Qin, Qiang Shi, and Juntao Huang. 2018. "Blind UAV Images Deblurring Based on Discriminative Networks" Sensors 18, no. 9: 2874.

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