Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling
Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Ch...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11023244/ |
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| author | Ahmad Hussain Mansoor Iqbal Sheraz Aslam Saad Alahmari Abdulwahab Alkharashi |
| author_facet | Ahmad Hussain Mansoor Iqbal Sheraz Aslam Saad Alahmari Abdulwahab Alkharashi |
| author_sort | Ahmad Hussain |
| collection | DOAJ |
| description | Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments. |
| format | Article |
| id | doaj-art-cf8630b029c54e7c92aafc731cb328b9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cf8630b029c54e7c92aafc731cb328b92025-08-20T03:20:29ZengIEEEIEEE Access2169-35362025-01-011310072010073010.1109/ACCESS.2025.357664611023244Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light HandlingAhmad Hussain0Mansoor Iqbal1Sheraz Aslam2https://orcid.org/0000-0003-4305-0908Saad Alahmari3https://orcid.org/0000-0001-9179-8326Abdulwahab Alkharashi4https://orcid.org/0009-0008-6618-2659Department of Computer Science, University of Wah, Wah, PakistanDepartment of Computer Science, University of Wah, Wah, PakistanDepartment of Computer Science, CTL Eurocollege, Limassol, CyprusDepartment of Computer Science, Applied College, Northern Border University, Arar, Saudi ArabiaDepartment of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaHaze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments.https://ieeexplore.ieee.org/document/11023244/Image dehazingdark channel prioratmospheric light estimationminimum fusion techniquevisual clarity |
| spellingShingle | Ahmad Hussain Mansoor Iqbal Sheraz Aslam Saad Alahmari Abdulwahab Alkharashi Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling IEEE Access Image dehazing dark channel prior atmospheric light estimation minimum fusion technique visual clarity |
| title | Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling |
| title_full | Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling |
| title_fullStr | Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling |
| title_full_unstemmed | Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling |
| title_short | Optimized Image Dehazing Using Dark Channel Prior With Minimum Fusion and Improved Atmospheric Light Handling |
| title_sort | optimized image dehazing using dark channel prior with minimum fusion and improved atmospheric light handling |
| topic | Image dehazing dark channel prior atmospheric light estimation minimum fusion technique visual clarity |
| url | https://ieeexplore.ieee.org/document/11023244/ |
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