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Information 2019, 10(2), 81;

Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Automation College, Beijing University of Posts and Telecommunications, Beijing, 100876, China, [email protected] (S.H.)
Beijing Key Laboratory of Control Technology for Toxic, Hazardous, Flammable and Explosive Sources of City, Beijing Municipal Institute of Labor Protection, Beijing, 100054, China
Author to whom correspondence should be addressed.
Received: 28 January 2019 / Revised: 19 February 2019 / Accepted: 22 February 2019 / Published: 25 February 2019


At present, the identification of haze levels mostly relies on traditional measurement methods, the real-time operation and convenience of these methods are poor. This paper aims to realize the identification of haze levels based on the method of haze images processing. Therefore, this paper divides the haze images into five levels, and obtains the high-quality haze images in each level by the brightness correction of the optimization solution and the color correction of the feature matching. At the same time, in order to reduce the noise of the haze images, this article improved the Butterworth filter. Finally, based on the processed haze images, this paper uses the Faster R-CNN network to identify the haze levels. The results of multiple sets of comparison experiments demonstrate the accuracy of the study. View Full-Text
Keywords: haze images; optimization correction; recognition of haze levels haze images; optimization correction; recognition of haze levels

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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).

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Hao, S.; Wang, P.; Hu, Y. Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction. Information 2019, 10, 81.

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