Nighttime Image Dehazing Based on Point Light Sources
Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10222 |
| _version_ | 1797475689906044928 |
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| author | Xin-Wei Yao Xinge Zhang Yuchen Zhang Weiwei Xing Xing Zhang |
| author_facet | Xin-Wei Yao Xinge Zhang Yuchen Zhang Weiwei Xing Xing Zhang |
| author_sort | Xin-Wei Yao |
| collection | DOAJ |
| description | Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely well studied for unmanned driving and nighttime surveillance, while the vast majority of dehazing algorithms in the past were only applicable to daytime conditions. After observing a large number of nighttime images, artificial light sources have replaced the position of the sun in daytime images and the impact of light sources on pixels varies with distance. This paper proposed a novel nighttime dehazing method using the light source influence matrix. The luminosity map can well express the photometric difference value of the picture light source. Then, the light source influence matrix is calculated to divide the image into near light source region and non-near light source region. Using the result of two regions, the two initial transmittances obtained by dark channel prior are fused by edge-preserving filtering. For the atmospheric light term, the initial atmospheric light value is corrected by the light source influence matrix. Finally, the final result is obtained by substituting the atmospheric light model. Theoretical analysis and comparative experiments verify the performance of the proposed image dehazing method. In terms of PSNR, SSIM, and UQI, this method improves 9.4%, 11.2%, and 3.3% over the existed night-time defogging method OSPF. In the future, we will explore the work from static picture dehazing to real-time video stream dehazing detection and will be used in detection on potential applications. |
| first_indexed | 2024-03-09T20:47:34Z |
| format | Article |
| id | doaj.art-53587f628ca24bd39028a3a376944b19 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:47:34Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-53587f628ca24bd39028a3a376944b192023-11-23T22:41:03ZengMDPI AGApplied Sciences2076-34172022-10-0112201022210.3390/app122010222Nighttime Image Dehazing Based on Point Light SourcesXin-Wei Yao0Xinge Zhang1Yuchen Zhang2Weiwei Xing3Xing Zhang4College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaZheJiang Wishare Technology Co., Ltd., Hangzhou 310023, ChinaImages routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely well studied for unmanned driving and nighttime surveillance, while the vast majority of dehazing algorithms in the past were only applicable to daytime conditions. After observing a large number of nighttime images, artificial light sources have replaced the position of the sun in daytime images and the impact of light sources on pixels varies with distance. This paper proposed a novel nighttime dehazing method using the light source influence matrix. The luminosity map can well express the photometric difference value of the picture light source. Then, the light source influence matrix is calculated to divide the image into near light source region and non-near light source region. Using the result of two regions, the two initial transmittances obtained by dark channel prior are fused by edge-preserving filtering. For the atmospheric light term, the initial atmospheric light value is corrected by the light source influence matrix. Finally, the final result is obtained by substituting the atmospheric light model. Theoretical analysis and comparative experiments verify the performance of the proposed image dehazing method. In terms of PSNR, SSIM, and UQI, this method improves 9.4%, 11.2%, and 3.3% over the existed night-time defogging method OSPF. In the future, we will explore the work from static picture dehazing to real-time video stream dehazing detection and will be used in detection on potential applications.https://www.mdpi.com/2076-3417/12/20/10222nighttime dehazephotometric mapatmospheric light model |
| spellingShingle | Xin-Wei Yao Xinge Zhang Yuchen Zhang Weiwei Xing Xing Zhang Nighttime Image Dehazing Based on Point Light Sources Applied Sciences nighttime dehaze photometric map atmospheric light model |
| title | Nighttime Image Dehazing Based on Point Light Sources |
| title_full | Nighttime Image Dehazing Based on Point Light Sources |
| title_fullStr | Nighttime Image Dehazing Based on Point Light Sources |
| title_full_unstemmed | Nighttime Image Dehazing Based on Point Light Sources |
| title_short | Nighttime Image Dehazing Based on Point Light Sources |
| title_sort | nighttime image dehazing based on point light sources |
| topic | nighttime dehaze photometric map atmospheric light model |
| url | https://www.mdpi.com/2076-3417/12/20/10222 |
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