Implementation of Haze Removal Algorithm to Enhance Low Light Images

K. Maheswari*, R. Charan Kadapa**
*-** Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India.
Periodicity:April - June'2022
DOI : https://doi.org/10.26634/jip.9.2.18796

Abstract

The image is captured in foggy atmospheric conditions, resulting in hazy, visually degraded visibility; it obscures image quality. Instead of producing clear images, pixel-based metrics are not guaranteed. This updated image is used as input in computer vision for low-level tasks like segmentation. To improve this, it introduces a new approach to de-hazing an image, the end-to-end approach, to keep the visual quality of the generated images. So, it takes one step further to explore the possibility of using the network to perform a semantic segmentation method with U-Net. U-Net will be built and used in this model to improve the quality of the output even more.

Keywords

Dehaze, Image Segmentation, U-Net, Atmospheric Scattering Model, Mean Square Error.

How to Cite this Article?

Maheswari, K., and Kadapa, R. C. (2022). Implementation of Haze Removal Algorithm to Enhance Low Light Images. i-manager’s Journal on Image Processing, 9(2), 44-49. https://doi.org/10.26634/jip.9.2.18796

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