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Transmission Map Refinement Using Laplacian Transform on Single Image Dehazing Based on Dark Channel Prior Approach Cover

Transmission Map Refinement Using Laplacian Transform on Single Image Dehazing Based on Dark Channel Prior Approach

Open Access
|Dec 2024

Abstract

Computer vision requires high-quality input images to facilitate image interpretation and analysis tasks. However, the image acquisition process does not always produce good-quality images. In outdoor environments, image quality is determined by weather or environmental conditions. Bad weather conditions due to pollution particles in the atmosphere such as smoke, fog, and haze can degrade image quality, such as contrast, brightness, and sharpness. This research proposes to obtain a better haze-free image from a hazy image by utilizing the Laplacian filtering and image enhancement techniques in the transmission map reconstruction based on the dark channel prior approach. Experimental results show that the proposed method could improve the visual quality of the dehazed images from 45% to 56% compared to the ground-truth images. The proposed method is also fairly competitive compared to similar methods in the same domain.

DOI: https://doi.org/10.2478/cait-2024-0039 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 126 - 142
Submitted on: Oct 14, 2024
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Accepted on: Nov 15, 2024
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Published on: Dec 18, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Lailia Rahmawati, Supriadi Rustad, Aris Marjuni, Mochammad Arief Soeleman, Catur Supriyanto, Guruh Fajar Shidik, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.