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References (26)
-
10.1117/1.1636183
-
S. Marín, F. Barrero, M. Vargas (2008)
Development of an Embedded Vision based Vehicle Detection System using an ARM Video Processor2008 11th International IEEE Conference on Intelligent Transportation Systems
-
Ming-Jung Seow, V. Asari (2006)
Ratio rule and homomorphic filter for enhancement of digital colour imageNeurocomputing, 69
-
Z. Rahman, D. Jobson, G. Woodell (2002)
Retinex processing for automatic image enhancement, 4662
-
Y. Schechner, S. Narasimhan, S. Nayar (2001)
Instant dehazing of images using polarizationProceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1
-
Dong Guangjun (2010)
Using Dark Channel Prior to Quickly Remove Haze from a Single ImageGeomatics and Information Science of Wuhan University
-
10.1145/1360612.1360671
-
Anat Levin, Dani Lischinski, Yair Weiss (2006)
A Closed-Form Solution to Natural Image MattingIEEE Transactions on Pattern Analysis and Machine Intelligence, 30
-
10.1109/TPAMI.2007.1177
-
S. Narasimhan, S. Nayar (2003)
Contrast Restoration of Weather Degraded ImagesIEEE Trans. Pattern Anal. Mach. Intell., 25
-
R. Tan (2008)
Visibility in bad weather from a single image2008 IEEE Conference on Computer Vision and Pattern Recognition
-
Kaiming He, Jian Sun, Xiaoou Tang (2009)
Single image haze removal using dark channel prior2009 IEEE Conference on Computer Vision and Pattern Recognition
-
Daniel Vlasic, Rolf Adelsberger, G. Vannucci, John Barnwell, M. Gross, W. Matusik, J. Popović (2007)
Practical motion capture in everyday surroundingsACM SIGGRAPH 2007 papers
-
Yen-Lin Chen, Chuan-Yen Chiang (2010)
Embedded vision-based nighttime driver assistance system2010 International Symposium on Computer, Communication, Control and Automation (3CA), 2
-
S. Shwartz, Einav Namer, Y. Schechner (2006)
Blind Haze Separation2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 2
-
S. Narasimhan (2003)
Interactive Deweathering of An Image Using Physical Model -
M. Marghany, M. Hashim, F. Moradi (2011)
Object recognitions in radarsat-1 sar data using fuzzy classificationInternational Journal of Physical Sciences, 6
-
10.1145/1276377.1276379
-
M. Marghany, M. Hashim (2010)
Developing adaptive algorithm for automatic detection of geological linear features using RADARSAT-1 SAR dataInternational Journal of Physical Sciences, 5
-
F. Guo, Zixing Cai, B. Xie, Jin Tang (2010)
Review and prospect of image dehazing techniques: Review and prospect of image dehazing techniquesJournal of Computer Applications, 30
-
Lu Yuan, Jian Sun, Long Quan, H. Shum (2007)
Image deblurring with blurred/noisy image pairsACM SIGGRAPH 2007 papers
-
Raanan Fattal (2008)
Single image dehazingACM SIGGRAPH 2008 papers
-
Tang Jin (2010)
Review and prospect of image dehazing techniquesJournal of Computer Applications
-
J. Oakley, H. Bu (2007)
Correction of Simple Contrast Loss in Color ImagesIEEE Transactions on Image Processing, 16
-
Maria Groszek, J. Allebach (2005)
Haze removal for image enhancement, 5678
-
Kaiming He, Jian Sun, Xiaoou Tang (2010)
Fast matting using large kernel matting Laplacian matrices2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- Emerald Publishing
- Copyright
- Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
- ISSN
- 0260-2288
- DOI
- 10.1108/SR-05-2012-684
- Publisher site
- See Article on Publisher Site
Abstract
Purpose – The purpose of this paper is to improve an image defogging algorithm based on a dark channel prior and use this method to clear the foggy image on the Advanced RISC (Reduced Instruction Set Computing) Machines (ARM) platform. Design/methodology/approach – The divided strategy of the foggy image was proposed through the estimation of the brightness and transmission thresholds. The two regions of the foggy image were processed using two transmissions. Many foggy images were tested using the improved method. Findings – The finding resulting from this study is that a divided strategy has been proposed to use the image defogging. Compared with the existing methods, the running time of the improved method is less on the ARM platform. Practical implications – Image enhancement is an important technology of digital images, and the quality of images plays a key role in the video monitoring and the intelligent transportation system. Originality/value – This paper presented an improved image defogging method using the divided strategy and a substantial number of experimental results was provided to demonstrate this method.
Journal
Sensor Review – Emerald Publishing
Published: Jun 10, 2014
Keywords: Improved image defogging; A divided strategy; ARM; Video monitoring system