Visible and Infrared Image Fusion Using Encoder-Decoder Network
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910739465240576 |
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| author | Ataman, Ferhat Can Akar, Gözde Bozdaği |
| author_facet | Ataman, Ferhat Can Akar, Gözde Bozdaği |
| contents | The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than state-of-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at \url{https://github.com/ferhatcan/pyFusionSR}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visible and Infrared Image Fusion Using Encoder-Decoder Network Ataman, Ferhat Can Akar, Gözde Bozdaği Computer Vision and Pattern Recognition Machine Learning Image and Video Processing The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than state-of-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at \url{https://github.com/ferhatcan/pyFusionSR}. |
| title | Visible and Infrared Image Fusion Using Encoder-Decoder Network |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2412.08073 |