Copyright © 2025 Authors retain the copyright of this article. This article 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.

@article{156020,
        author = {JYOTHI M and Nandini B M},
        title = {Fog Density Estimation and Elimination of Fog in Single Image  },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {516-521},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156020},
        abstract = {In the present work, we empirically study the problem in the important task of image fog and remove the fog from a picture using an image defogging algorithm. Dirt, fumes, and other fragments distort the perception of the sky, reducing clarity. Foggy pictures generate a variety of vision issues for drivers and visitors everywhere, particularly in mountainous places where there is a lot of Haze and fog. So, a fog removal algorithm can reduce these types of impacts. Outdoor photographs were utilized with certain filters be concerned to locate the mist in the image. In RGB (Red, Blue, Green) channels, any one of the color channels has a modest value in hazy/ foggy photos. The air-light depth map is primarily responsible for the intensity of these pixels. Obtaining a good quality dehazed image requires approximating these low-value spots on the mist transmission map. To get a high-standard defogged image, a training model is used. The method has been tested on datasets that contain outdoor photos. This method also enhances the visibility of an image.},
        keywords = {Defogging, Single Image, Image enhancement, trained model, PSNR.},
        month = {},
        }