Blind UAV Images Deblurring Based on Discriminative Networks
AbstractUnmanned 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
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Wang, R.; Ma, G.; Qin, Q.; Shi, Q.; Huang, J. Blind UAV Images Deblurring Based on Discriminative Networks. Sensors 2018, 18, 2874.
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; Ma, Guorui; Qin, Qianqing; Shi, Qiang; Huang, Juntao. 2018. "Blind UAV Images Deblurring Based on Discriminative Networks." Sensors 18, no. 9: 2874.
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