Next Article in Journal
Fabrication of a Urea Biosensor for Real-Time Dynamic Fluid Measurement
Previous Article in Journal
A “Turn-On” Fluorescence Copper Biosensor Based on DNA Cleavage-Dependent Graphene Oxide-dsDNA-CdTe Quantum Dots Complex
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(8), 2606; https://doi.org/10.3390/s18082606

A Sensor Image Dehazing Algorithm Based on Feature Learning

College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China
*
Authors to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 6 August 2018 / Accepted: 6 August 2018 / Published: 9 August 2018
(This article belongs to the Section Intelligent Sensors)
View Full-Text   |   Download PDF [5448 KB, uploaded 9 August 2018]   |  

Abstract

To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images. View Full-Text
Keywords: image dehazing; feature learning; sparse coding; generative adversarial networks image dehazing; feature learning; sparse coding; generative adversarial networks
Figures

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, K.; He, L.; Ma, S.; Gao, S.; Bi, D. A Sensor Image Dehazing Algorithm Based on Feature Learning. Sensors 2018, 18, 2606.

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

1

Comments

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