Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/22/10111 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850267663815671808 |
|---|---|
| author | Libang Chen Jinyan Lin Qihang Bian Yikun Liu Jianying Zhou |
| author_facet | Libang Chen Jinyan Lin Qihang Bian Yikun Liu Jianying Zhou |
| author_sort | Libang Chen |
| collection | DOAJ |
| description | Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications. |
| format | Article |
| id | doaj-art-81fbe2eea2b34f519a80c9a2ecdc3d8d |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-81fbe2eea2b34f519a80c9a2ecdc3d8d2025-08-20T01:53:42ZengMDPI AGApplied Sciences2076-34172024-11-0114221011110.3390/app142210111Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility ConditionLibang Chen0Jinyan Lin1Qihang Bian2Yikun Liu3Jianying Zhou4Guangdong Provincial Key Laboratory of Quantum Metrology and Sensing, School of Physics and Astronomy, Sun Yat-Sen University, Zhuhai Campus, Zhuhai 519082, ChinaSchool of Artificial Intelligence, Sun Yat-Sen University, Zhuhai Campus, Zhuhai 519082, ChinaGuangdong Provincial Key Laboratory of Quantum Metrology and Sensing, School of Physics and Astronomy, Sun Yat-Sen University, Zhuhai Campus, Zhuhai 519082, ChinaGuangdong Provincial Key Laboratory of Quantum Metrology and Sensing, School of Physics and Astronomy, Sun Yat-Sen University, Zhuhai Campus, Zhuhai 519082, ChinaState Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-Sen University, Guangzhou 510275, ChinaImaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.https://www.mdpi.com/2076-3417/14/22/10111Image enhancementlow visibility imagingoptical imagingatmospheric scattering |
| spellingShingle | Libang Chen Jinyan Lin Qihang Bian Yikun Liu Jianying Zhou Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition Applied Sciences Image enhancement low visibility imaging optical imaging atmospheric scattering |
| title | Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition |
| title_full | Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition |
| title_fullStr | Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition |
| title_full_unstemmed | Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition |
| title_short | Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition |
| title_sort | inhomogeneous illumination image enhancement under extremely low visibility condition |
| topic | Image enhancement low visibility imaging optical imaging atmospheric scattering |
| url | https://www.mdpi.com/2076-3417/14/22/10111 |
| work_keys_str_mv | AT libangchen inhomogeneousilluminationimageenhancementunderextremelylowvisibilitycondition AT jinyanlin inhomogeneousilluminationimageenhancementunderextremelylowvisibilitycondition AT qihangbian inhomogeneousilluminationimageenhancementunderextremelylowvisibilitycondition AT yikunliu inhomogeneousilluminationimageenhancementunderextremelylowvisibilitycondition AT jianyingzhou inhomogeneousilluminationimageenhancementunderextremelylowvisibilitycondition |