Single-frame multi-exposure image fusion via narrowband filter decoupled imaging
Multi-exposure image fusion (MEF) can efficiently enhance the dynamic range of image. It can break through the physical imaging limitations inherent in photoelectric sensors. However, the single-camera multi-exposure method requires a time investment, while the multi-camera single-exposure approach...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 625; s. 129441 |
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| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
07.04.2025
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| ISSN: | 0925-2312 |
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| Abstract | Multi-exposure image fusion (MEF) can efficiently enhance the dynamic range of image. It can break through the physical imaging limitations inherent in photoelectric sensors. However, the single-camera multi-exposure method requires a time investment, while the multi-camera single-exposure approach is subject to imaging conditions. In this paper, we propose a single-frame decoupled imaging method that enables acquiring multiple differently exposed images from a single exposure captured by one color camera. The method leverages the physical imaging process of a color camera, decoupling the narrowband filtered RAW data into multiple exposure images by exploiting the variations in quantum efficiency distributions. And based on this approach, we construct a decomposed single-frame (DSF) images dataset. The sequence of images within this dataset are naturally spatio-temporally consistent and no longer require registration. Furthermore, a decomposed single-frame MEF network is proposed, termed as DSF-MEF, which employs a hierarchical encoder-decoder structure to predict exposure weight mappings. Specifically, we design a residual mixed attention module (RMAM) for exposure weight prediction. It uses channel and spatial domain attention mechanisms and residual jump connections to perform feature extraction. Subsequently, to improve the overall spatial continuity representation of the exposure weight map sequence, we construct multiscale feature integration module (MFIM) to capture exposure information at different resolution scales. A loss function composed of image structural similarity, gradient texture similarity, and pixel intensity is designed to comprehensively optimize fusion performance. Experimental results show that our method not only achieves single-frame HDR fusion imaging, but also achieves better fusion visual effects compared to other advanced methods. |
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| AbstractList | Multi-exposure image fusion (MEF) can efficiently enhance the dynamic range of image. It can break through the physical imaging limitations inherent in photoelectric sensors. However, the single-camera multi-exposure method requires a time investment, while the multi-camera single-exposure approach is subject to imaging conditions. In this paper, we propose a single-frame decoupled imaging method that enables acquiring multiple differently exposed images from a single exposure captured by one color camera. The method leverages the physical imaging process of a color camera, decoupling the narrowband filtered RAW data into multiple exposure images by exploiting the variations in quantum efficiency distributions. And based on this approach, we construct a decomposed single-frame (DSF) images dataset. The sequence of images within this dataset are naturally spatio-temporally consistent and no longer require registration. Furthermore, a decomposed single-frame MEF network is proposed, termed as DSF-MEF, which employs a hierarchical encoder-decoder structure to predict exposure weight mappings. Specifically, we design a residual mixed attention module (RMAM) for exposure weight prediction. It uses channel and spatial domain attention mechanisms and residual jump connections to perform feature extraction. Subsequently, to improve the overall spatial continuity representation of the exposure weight map sequence, we construct multiscale feature integration module (MFIM) to capture exposure information at different resolution scales. A loss function composed of image structural similarity, gradient texture similarity, and pixel intensity is designed to comprehensively optimize fusion performance. Experimental results show that our method not only achieves single-frame HDR fusion imaging, but also achieves better fusion visual effects compared to other advanced methods. |
| ArticleNumber | 129441 |
| Author | Gong, Shuaifeng Wei, Duan Ke, Xin Xiong, Fengchao Wu, Zijian Peng, Yong Lu, Jun Han, Jing Zhang, Yan Zhao, Zhuang Bai, Lianfa |
| Author_xml | – sequence: 1 givenname: Zhuang surname: Zhao fullname: Zhao, Zhuang email: zhaozhuang@njust.edu.cn organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 2 givenname: Xin surname: Ke fullname: Ke, Xin organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 3 givenname: Jing surname: Han fullname: Han, Jing organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 4 givenname: Zijian surname: Wu fullname: Wu, Zijian organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 5 givenname: Jun surname: Lu fullname: Lu, Jun organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 6 givenname: Lianfa surname: Bai fullname: Bai, Lianfa organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 7 givenname: Shuaifeng surname: Gong fullname: Gong, Shuaifeng organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 8 givenname: Yan surname: Zhang fullname: Zhang, Yan organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 9 givenname: Yong surname: Peng fullname: Peng, Yong organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 10 givenname: Fengchao orcidid: 0000-0002-9753-4919 surname: Xiong fullname: Xiong, Fengchao organization: Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 11 givenname: Duan orcidid: 0009-0008-0880-3592 surname: Wei fullname: Wei, Duan organization: Wuhan University of Science and Technology, Wuhan 430081, China |
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| Keywords | Multi-scale feature integration Single-frame HDR imaging Quantum efficiency Multi-exposure image fusion |
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