Next Article in Journal
Effect of Metal Thickness on the Sensitivity of Crack-Based Sensors
Next Article in Special Issue
A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images
Previous Article in Journal
Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis
Previous Article in Special Issue
RPC-Based Orthorectification for Satellite Images Using FPGA
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 18
Issue 9
10.3390/s18092874
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Blind UAV Images Deblurring Based on Discriminative Networks

by
Ruihua Wang
1,
Guorui Ma
1,*,
Qianqing Qin
1,
Qiang Shi
2 and
Juntao Huang
1
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
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; https://doi.org/10.3390/s18092874
Submission 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)
Browse Figures
Versions Notes

Abstract

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.
Keywords: UAV images; image deblurring; image prior; discriminative networks UAV images; image deblurring; image prior; discriminative networks

Share and Cite

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. https://doi.org/10.3390/s18092874

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. https://doi.org/10.3390/s18092874

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. https://doi.org/10.3390/s18092874

APA Style

Wang, R., Ma, G., Qin, Q., Shi, Q., & Huang, J. (2018). Blind UAV Images Deblurring Based on Discriminative Networks. Sensors, 18(9), 2874. https://doi.org/10.3390/s18092874

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop