AEFusion: Adaptive Enhanced Fusion of Visible and Infrared Images for Night Vision
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| Udgivet i: | Remote Sensing vol. 17, no. 18 (2025), p. 3129-3154 |
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| Andre forfattere: | , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17183129 |2 doi | |
| 035 | |a 3254636596 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Wang, Xiaozhu |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; wangxz@aircas.ac.cn (X.W.); wenqin@aircas.ac.cn (Q.W.); zhanggf@aircas.ac.cn (G.Z.); huangmin@aircas.ac.cn (M.H.) | |
| 245 | 1 | |a AEFusion: Adaptive Enhanced Fusion of Visible and Infrared Images for Night Vision | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>We propose a deep learning-based visible–infrared fusion framework with local adaptive enhancement and ResNet152-LDA feature integration. <list-item> Our method achieves superior performance over state-of-the-art methods in both objective metrics and subjective visual quality. </list-item> What is the implication of the main findings? <list list-type="bullet"> <list-item> </list-item>Provides a robust solution for preserving critical details in night vision image fusion. <list-item> Offers practical support for intelligent driving and low-visibility imaging applications. </list-item> Under night vision conditions, visible-spectrum images often fail to capture background details. Conventional visible and infrared fusion methods generally overlay thermal signatures without preserving latent features in low-visibility regions. This paper proposes a novel deep learning-based fusion algorithm to enhance visual perception in night driving scenarios. Firstly, a local adaptive enhancement algorithm corrects underexposed and overexposed regions in visible images, thereby preventing oversaturation during brightness adjustment. Secondly, ResNet152 extracts hierarchical feature maps from enhanced visible and infrared inputs. Max pooling and average pooling operations preserve critical features and distinct information across these feature maps. Finally, Linear Discriminant Analysis (LDA) reduces dimensionality and decorrelates features. We reconstruct the fused image by the weighted integration of the source images. The experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods in both objective metrics and subjective visual assessments. | |
| 653 | |a Visual perception | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Algorithms | ||
| 653 | |a Vision systems | ||
| 653 | |a Vision | ||
| 653 | |a Infrared imagery | ||
| 653 | |a Computer vision | ||
| 653 | |a Discriminant analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Radiation | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Remote sensing | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Neural networks | ||
| 653 | |a Visibility | ||
| 653 | |a Night vision | ||
| 653 | |a Feature maps | ||
| 653 | |a Infrared signatures | ||
| 653 | |a Methods | ||
| 700 | 1 | |a Zhang, Chenglong |u The School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China | |
| 700 | 1 | |a Hu, Jianming |u The School of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001, China; hujianming@hit.edu.cn | |
| 700 | 1 | |a Qin, Wen |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; wangxz@aircas.ac.cn (X.W.); wenqin@aircas.ac.cn (Q.W.); zhanggf@aircas.ac.cn (G.Z.); huangmin@aircas.ac.cn (M.H.) | |
| 700 | 1 | |a Zhang, Guifeng |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; wangxz@aircas.ac.cn (X.W.); wenqin@aircas.ac.cn (Q.W.); zhanggf@aircas.ac.cn (G.Z.); huangmin@aircas.ac.cn (M.H.) | |
| 700 | 1 | |a Huang, Min |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; wangxz@aircas.ac.cn (X.W.); wenqin@aircas.ac.cn (Q.W.); zhanggf@aircas.ac.cn (G.Z.); huangmin@aircas.ac.cn (M.H.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 18 (2025), p. 3129-3154 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254636596/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254636596/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |