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Volume - 3 | Issue - 3 | september 2021

Hybrid Approach for Image Defogging Process based on Atmospheric Light Estimation Process
Akey Sungheetha   244  178
Pages: 184-195
Cite this article
Sungheetha, A. (2021). Hybrid Approach for Image Defogging Process based on Atmospheric Light Estimation Process. Journal of Artificial Intelligence and Capsule Networks, 3(3), 184-195. doi:10.36548/jaicn.2021.3.003
Published
28 August, 2021
Abstract

Due to unfavorable weather circumstances, images captured from multiple sensors have limited the contrast and visibility. Many applications, such as web camera surveillance in public locations are used to identify object categorization and capture a vehicle's licence plate in order to detect reckless driving. The traditional methods can improve the image quality by incorporating luminance, minimizing distortion, and removing unwanted visual effects from the given images. Dehazing is a vital step in the image defogging process of many real-time applications. This research article focuses on the prediction of transmission maps in the process of image defogging through the combination of dark channel prior (DCP), transmission map with refinement, and atmospheric light estimation process. This framework has succeeded in the prior segmentation process for obtaining a better visualization. This prediction of transmission maps can be improved through the statistical process of obtaining higher accuracy for the proposed model. This improvement can be achieved by incorporating the proposed framework with an atmospheric light estimation algorithm. Finally, the experimental results show that the proposed deep learning model is achieving a superior performance when compared to other traditional algorithms.

Keywords

Image defogging deep learning

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