VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment
Infrared (IR) and visible (VIS) image fusion enhances vision tasks by combining complementary data. However, most existing methods assume normal lighting conditions and thus perform poorly in low-light environments, where VIS images often lose critical texture details. To address this limitation, we...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6295 |
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| author | Jiawen Li Zhengzhong Huang Jiapin Peng Xiaochuan Zhang Rongzhu Zhang |
| author_facet | Jiawen Li Zhengzhong Huang Jiapin Peng Xiaochuan Zhang Rongzhu Zhang |
| author_sort | Jiawen Li |
| collection | DOAJ |
| description | Infrared (IR) and visible (VIS) image fusion enhances vision tasks by combining complementary data. However, most existing methods assume normal lighting conditions and thus perform poorly in low-light environments, where VIS images often lose critical texture details. To address this limitation, we propose VCAFusion, a novel approach for robust infrared and visible image fusion in low-light scenarios. Our framework incorporates an adaptive brightness adjustment model based on light reflection theory to mitigate illumination-induced degradation in nocturnal images. Additionally, we design an adaptive enhancement function inspired by human visual perception to recover weak texture details. To further improve fusion quality, we develop an edge-preserving multi-scale decomposition model and a saliency-preserving strategy, ensuring seamless integration of perceptual features. By effectively balancing low-light enhancement and fusion, our framework preserves both the intensity distribution and the fine texture details of salient objects. Extensive experiments on public datasets demonstrate that VCAFusion achieves superior fusion quality, closely aligning with human visual perception and outperforming state-of-the-art methods in both qualitative and quantitative evaluations. |
| format | Article |
| id | doaj-art-9c5107171e6b45b9b7adc3248a90c96f |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9c5107171e6b45b9b7adc3248a90c96f2025-08-20T02:23:00ZengMDPI AGApplied Sciences2076-34172025-06-011511629510.3390/app15116295VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics AdjustmentJiawen Li0Zhengzhong Huang1Jiapin Peng2Xiaochuan Zhang3Rongzhu Zhang4Southwest Institute of Technical Physics, Chengdu 610041, ChinaSouthwest Institute of Technical Physics, Chengdu 610041, ChinaSouthwest Institute of Technical Physics, Chengdu 610041, ChinaSouthwest Institute of Technical Physics, Chengdu 610041, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaInfrared (IR) and visible (VIS) image fusion enhances vision tasks by combining complementary data. However, most existing methods assume normal lighting conditions and thus perform poorly in low-light environments, where VIS images often lose critical texture details. To address this limitation, we propose VCAFusion, a novel approach for robust infrared and visible image fusion in low-light scenarios. Our framework incorporates an adaptive brightness adjustment model based on light reflection theory to mitigate illumination-induced degradation in nocturnal images. Additionally, we design an adaptive enhancement function inspired by human visual perception to recover weak texture details. To further improve fusion quality, we develop an edge-preserving multi-scale decomposition model and a saliency-preserving strategy, ensuring seamless integration of perceptual features. By effectively balancing low-light enhancement and fusion, our framework preserves both the intensity distribution and the fine texture details of salient objects. Extensive experiments on public datasets demonstrate that VCAFusion achieves superior fusion quality, closely aligning with human visual perception and outperforming state-of-the-art methods in both qualitative and quantitative evaluations.https://www.mdpi.com/2076-3417/15/11/6295image fusionadaptive low-light adjustmentlocal detailsaliency-preservingmulti-scale decomposition |
| spellingShingle | Jiawen Li Zhengzhong Huang Jiapin Peng Xiaochuan Zhang Rongzhu Zhang VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment Applied Sciences image fusion adaptive low-light adjustment local detail saliency-preserving multi-scale decomposition |
| title | VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment |
| title_full | VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment |
| title_fullStr | VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment |
| title_full_unstemmed | VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment |
| title_short | VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment |
| title_sort | vcafusion a framework for infrared and low light visible image fusion based on visual characteristics adjustment |
| topic | image fusion adaptive low-light adjustment local detail saliency-preserving multi-scale decomposition |
| url | https://www.mdpi.com/2076-3417/15/11/6295 |
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