Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Shengyu Hao,Yanzhu Hu,Peiyi Wang

doi:10.3390/info10020081

Shengyu Hao, Yanzhu Hu + Show 1 more

Open Access

https://doi.org/10.3390/info10020081

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Abstract

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.

Highlights

Nowadays, due to the continuous advancement of urban industrialization, the haze problem has seriously affected the daily life of human beings
The final research of this paper aims to correct the brightness and color difference between the same level of haze images, and design a filter that can reduce the haze images noise, and using the Faster R-convolutional neural networks (CNN) network to realize the identification of the level of haze
This paper proposes a method of haze images recognition based on brightness optimization feedback and color correction, which solves the problem that the traditional technology cannot monitor the haze levels in an efficient and real-time method

Summary

Introduction

Due to the continuous advancement of urban industrialization, the haze problem has seriously affected the daily life of human beings. The formation of haze can lead to a decrease in visibility, and different levels of haze can cause huge brightness differences and chromatic aberrations in the surrounding environment. The current research has no suitable solution for the huge interference of brightness and chromatic aberration on the haze images, which seriously affects the accuracy of haze level recognition. This paper proposes the correction and recognition of the same ambient haze images based on optimal feedback, aiming to correct the error caused by illumination and imaging conditions in the same environment, and to identify the haze level based on the corrected image. Lu Lipeng et al realized the classification and recognition of haze pollution level based on image gray difference statistics [1]. Ji Dabo team achieved the measurement of soot environment by establishing a model of the relationship between image gray value and dust concentration [2]

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