Image Clarification Algorithm for Adverse Weather

In the current era, adverse weather conditions such as dust storms and haze occur frequently. Addressing issues like color bias, uneven color distribution, low contrast, and brightness in images affected by such conditions, we propose an image clarification algorithm tailored for adverse weather. Sp...

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Bibliographic Details
Published in2024 5th International Conference on Computer Engineering and Application (ICCEA) pp. 1032 - 1037
Main Authors Zhang, Yinzhou, Menghe, Jiya
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.04.2024
Subjects
Accuracy
Adaptation models
Analytical models
Dark Channel Prior
Gaussian
Gray World based on Lab
Histograms
Image color analysis
Measurement
MSRCR
Severe Weather
Storms
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Summary:In the current era, adverse weather conditions such as dust storms and haze occur frequently. Addressing issues like color bias, uneven color distribution, low contrast, and brightness in images affected by such conditions, we propose an image clarification algorithm tailored for adverse weather. Specifically, for the issue of color bias, we introduce a color correction algorithm based on the Lab color space and a Weighted Fusion Gaussian Model. This algorithm surpasses traditional methods in terms of color correction accuracy and adaptability. To tackle the problems of dull colors and low clarity, we propose a color restoration algorithm based on an improved Multi-Scale Retinex with Color Restoration (MSRCR). Lastly, image dehazing and brightness enhancement are achieved through an enhanced algorithm based on the Dark Channel Prior. Experimental results and analysis demonstrate that our approach not only effectively corrects color bias but also enhances image contrast and clarity, significantly improving the clarification performance of the base model.
ISSN:2159-1288
DOI:10.1109/ICCEA62105.2024.10603821