Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement
Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/12/2069 |
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| author | Xiaohang Zhao Liang Huang Mingxuan Li Chengshan Han Ting Nie |
| author_facet | Xiaohang Zhao Liang Huang Mingxuan Li Chengshan Han Ting Nie |
| author_sort | Xiaohang Zhao |
| collection | DOAJ |
| description | Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions. |
| format | Article |
| id | doaj-art-df5b3cea07e04c68a6997fa046989313 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-df5b3cea07e04c68a6997fa0469893132025-08-20T03:29:47ZengMDPI AGRemote Sensing2072-42922025-06-011712206910.3390/rs17122069Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image EnhancementXiaohang Zhao0Liang Huang1Mingxuan Li2Chengshan Han3Ting Nie4Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaEnhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions.https://www.mdpi.com/2072-4292/17/12/2069computer visionvariational methodnon-uniform enhancementremote sensing |
| spellingShingle | Xiaohang Zhao Liang Huang Mingxuan Li Chengshan Han Ting Nie Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement Remote Sensing computer vision variational method non-uniform enhancement remote sensing |
| title | Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement |
| title_full | Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement |
| title_fullStr | Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement |
| title_full_unstemmed | Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement |
| title_short | Atmospheric Scattering Model and Non-Uniform Illumination Compensation for Low-Light Remote Sensing Image Enhancement |
| title_sort | atmospheric scattering model and non uniform illumination compensation for low light remote sensing image enhancement |
| topic | computer vision variational method non-uniform enhancement remote sensing |
| url | https://www.mdpi.com/2072-4292/17/12/2069 |
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