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
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(9), 2874;

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.
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)
Full-Text   |   PDF [9788 KB, uploaded 31 August 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top