Transmission Map Refinement Using Laplacian Transform on Single Image Dehazing Based on Dark Channel Prior Approach
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 condi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-12-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2024-0039 |
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| Summary: | 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. |
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| ISSN: | 1314-4081 |