Next Article in Journal
Effects of Warming Hiatuses on Vegetation Growth in the Northern Hemisphere
Next Article in Special Issue
Optimal Seamline Detection for Orthoimage Mosaicking Based on DSM and Improved JPS Algorithm
Previous Article in Journal
Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry
Previous Article in Special Issue
Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

1
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, 1065 av. de la Médecine, Quebec City, QC G1V 0A6, Canada
3
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 682; https://doi.org/10.3390/rs10050682
Received: 9 March 2018 / Revised: 8 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
  |  
PDF [20737 KB, uploaded 3 May 2018]
  |  

Abstract

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods. View Full-Text
Keywords: infrared image enhancement; adaptive histogram partition; local contrast weighted distribution; brightness correction; Smart City infrared image enhancement; adaptive histogram partition; local contrast weighted distribution; brightness correction; Smart City
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

Wan, M.; Gu, G.; Qian, W.; Ren, K.; Chen, Q.; Maldague, X. Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction. Remote Sens. 2018, 10, 682.

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]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top