Enhance Low Visibility Image Using Haze-Removal Framework
We proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness. Dehazing and luminance enhancement algorithms are considered standard technique...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10271284/ |
| _version_ | 1797655685384634368 |
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| author | Ping Juei Liu |
| author_facet | Ping Juei Liu |
| author_sort | Ping Juei Liu |
| collection | DOAJ |
| description | We proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness. Dehazing and luminance enhancement algorithms are considered standard techniques to overcome these issues. However, natural environments usually involve several unfavorable conditions simultaneously, such as insufficient illumination, blur caused by the haze, and color cast resulting from the sun or scattering; this makes dehazing algorithms challenging to overcome environmental issues. Besides, dehazing algorithms sometimes result in artifacts. The proposed framework solves these issues simultaneously by implementing a double-side enhancement in contrast and brightness based on a new dehazing algorithm. We compare the new dehazing algorithm with others using full-reference benchmarks to ensure performance stability. Afterward, to show the advantage of using the new dehazing algorithm, we evaluate the compatibility between the proposed framework and all dehazing algorithms using non-reference benchmarks. At last, we pair dehazing and luminance enhancement algorithms and compare the combinations with the proposed framework. Eventually, experimental results prove that the new dehazing algorithm outperforms others and is better compatible with the proposed framework. Meanwhile, the proposed framework is superior in contrast and brightness enhancements and outperforms the single dehazing algorithm or the combinations. |
| first_indexed | 2024-03-11T17:18:10Z |
| format | Article |
| id | doaj.art-fd157a00557c461a93103a8c2e5d85e5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:18:10Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fd157a00557c461a93103a8c2e5d85e52023-10-19T23:00:38ZengIEEEIEEE Access2169-35362023-01-011111345011346310.1109/ACCESS.2023.332204110271284Enhance Low Visibility Image Using Haze-Removal FrameworkPing Juei Liu0https://orcid.org/0000-0002-6609-4850Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, TaiwanWe proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness. Dehazing and luminance enhancement algorithms are considered standard techniques to overcome these issues. However, natural environments usually involve several unfavorable conditions simultaneously, such as insufficient illumination, blur caused by the haze, and color cast resulting from the sun or scattering; this makes dehazing algorithms challenging to overcome environmental issues. Besides, dehazing algorithms sometimes result in artifacts. The proposed framework solves these issues simultaneously by implementing a double-side enhancement in contrast and brightness based on a new dehazing algorithm. We compare the new dehazing algorithm with others using full-reference benchmarks to ensure performance stability. Afterward, to show the advantage of using the new dehazing algorithm, we evaluate the compatibility between the proposed framework and all dehazing algorithms using non-reference benchmarks. At last, we pair dehazing and luminance enhancement algorithms and compare the combinations with the proposed framework. Eventually, experimental results prove that the new dehazing algorithm outperforms others and is better compatible with the proposed framework. Meanwhile, the proposed framework is superior in contrast and brightness enhancements and outperforms the single dehazing algorithm or the combinations.https://ieeexplore.ieee.org/document/10271284/Image enhancementcontrast enhancementbrightness enhancementlow-visibility imagehaze removaldehaze |
| spellingShingle | Ping Juei Liu Enhance Low Visibility Image Using Haze-Removal Framework IEEE Access Image enhancement contrast enhancement brightness enhancement low-visibility image haze removal dehaze |
| title | Enhance Low Visibility Image Using Haze-Removal Framework |
| title_full | Enhance Low Visibility Image Using Haze-Removal Framework |
| title_fullStr | Enhance Low Visibility Image Using Haze-Removal Framework |
| title_full_unstemmed | Enhance Low Visibility Image Using Haze-Removal Framework |
| title_short | Enhance Low Visibility Image Using Haze-Removal Framework |
| title_sort | enhance low visibility image using haze removal framework |
| topic | Image enhancement contrast enhancement brightness enhancement low-visibility image haze removal dehaze |
| url | https://ieeexplore.ieee.org/document/10271284/ |
| work_keys_str_mv | AT pingjueiliu enhancelowvisibilityimageusinghazeremovalframework |