Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation
This paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insig...
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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/10818626/ |
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| author | Truong-Dong do Le-Anh Tran Seokyong Moon Jio Chung Ngoc-Phi Nguyen Sung Kyung Hong |
| author_facet | Truong-Dong do Le-Anh Tran Seokyong Moon Jio Chung Ngoc-Phi Nguyen Sung Kyung Hong |
| author_sort | Truong-Dong do |
| collection | DOAJ |
| description | This paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insights derived from the HSV color space: first, the hue component shows negligible variation between corresponding hazy and haze-free points; and second, in the 2D saturation-value coordinate system, the majority of lines connecting hazy-clean point pairs tend to converge near the atmospheric light coordinates. Consequently, haze removal can be achieved through appropriate translations within the saturation-value coordinates. Additionally, a robust soft segmentation method that employs a morphological min-max channel is integrated into the framework. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is established. Experimental evaluations across various datasets demonstrate that the proposed approach effectively mitigates color distortion and successfully restores visually appealing images. Moreover, a case study involving actual flight test demonstrates the feasibility and effectiveness of the proposed approach in real-world scenarios. The code is available at <uri>https://github.com/tranleanh/rsvt</uri>. |
| format | Article |
| id | doaj-art-12fbd832d3574acea32c3a9ebf082cdb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-12fbd832d3574acea32c3a9ebf082cdb2025-01-15T00:02:41ZengIEEEIEEE Access2169-35362025-01-01138060807610.1109/ACCESS.2024.352431610818626Drone-View Haze Removal via Regional Saturation-Value Translation and Soft SegmentationTruong-Dong do0https://orcid.org/0000-0001-8178-0018Le-Anh Tran1https://orcid.org/0000-0002-9380-7166Seokyong Moon2https://orcid.org/0009-0008-5350-8592Jio Chung3https://orcid.org/0009-0000-1058-5733Ngoc-Phi Nguyen4https://orcid.org/0000-0002-5100-6264Sung Kyung Hong5https://orcid.org/0000-0003-1143-2194Department of Aerospace Systems Engineering, Sejong University, Seoul, South KoreaDepartment of Research and Development, Mindintech Inc., Seoul, South KoreaDepartment of Research and Development, Mindintech Inc., Seoul, South KoreaDepartment of Research and Development, Mindintech Inc., Seoul, South KoreaInstitute of Mechanical and Electrical Engineering, Center for Industrial Mechanics, University of Southern Denmark, Sønderborg, DenmarkDepartment of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South KoreaThis paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insights derived from the HSV color space: first, the hue component shows negligible variation between corresponding hazy and haze-free points; and second, in the 2D saturation-value coordinate system, the majority of lines connecting hazy-clean point pairs tend to converge near the atmospheric light coordinates. Consequently, haze removal can be achieved through appropriate translations within the saturation-value coordinates. Additionally, a robust soft segmentation method that employs a morphological min-max channel is integrated into the framework. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is established. Experimental evaluations across various datasets demonstrate that the proposed approach effectively mitigates color distortion and successfully restores visually appealing images. Moreover, a case study involving actual flight test demonstrates the feasibility and effectiveness of the proposed approach in real-world scenarios. The code is available at <uri>https://github.com/tranleanh/rsvt</uri>.https://ieeexplore.ieee.org/document/10818626/Dehazing priorhaze removalimage defoggingimage dehazingimage restoration |
| spellingShingle | Truong-Dong do Le-Anh Tran Seokyong Moon Jio Chung Ngoc-Phi Nguyen Sung Kyung Hong Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation IEEE Access Dehazing prior haze removal image defogging image dehazing image restoration |
| title | Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation |
| title_full | Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation |
| title_fullStr | Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation |
| title_full_unstemmed | Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation |
| title_short | Drone-View Haze Removal via Regional Saturation-Value Translation and Soft Segmentation |
| title_sort | drone view haze removal via regional saturation value translation and soft segmentation |
| topic | Dehazing prior haze removal image defogging image dehazing image restoration |
| url | https://ieeexplore.ieee.org/document/10818626/ |
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