Learning to Remove Wrinkled Transparent Film with Polarized Prior
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910357105147904 |
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| author | Tang, Jiaqi Wu, Ruizheng Xu, Xiaogang Hu, Sixing Chen, Ying-Cong |
| author_facet | Tang, Jiaqi Wu, Ruizheng Xu, Xiaogang Hu, Sixing Chen, Ying-Cong |
| contents | In this paper, we study a new problem, Film Removal (FR), which attempts to remove the interference of wrinkled transparent films and reconstruct the original information under films for industrial recognition systems. We first physically model the imaging of industrial materials covered by the film. Considering the specular highlight from the film can be effectively recorded by the polarized camera, we build a practical dataset with polarization information containing paired data with and without transparent film. We aim to remove interference from the film (specular highlights and other degradations) with an end-to-end framework. To locate the specular highlight, we use an angle estimation network to optimize the polarization angle with the minimized specular highlight. The image with minimized specular highlight is set as a prior for supporting the reconstruction network. Based on the prior and the polarized images, the reconstruction network can decouple all degradations from the film. Extensive experiments show that our framework achieves SOTA performance in both image reconstruction and industrial downstream tasks. Our code will be released at \url{https://github.com/jqtangust/FilmRemoval}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04368 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Remove Wrinkled Transparent Film with Polarized Prior Tang, Jiaqi Wu, Ruizheng Xu, Xiaogang Hu, Sixing Chen, Ying-Cong Computer Vision and Pattern Recognition In this paper, we study a new problem, Film Removal (FR), which attempts to remove the interference of wrinkled transparent films and reconstruct the original information under films for industrial recognition systems. We first physically model the imaging of industrial materials covered by the film. Considering the specular highlight from the film can be effectively recorded by the polarized camera, we build a practical dataset with polarization information containing paired data with and without transparent film. We aim to remove interference from the film (specular highlights and other degradations) with an end-to-end framework. To locate the specular highlight, we use an angle estimation network to optimize the polarization angle with the minimized specular highlight. The image with minimized specular highlight is set as a prior for supporting the reconstruction network. Based on the prior and the polarized images, the reconstruction network can decouple all degradations from the film. Extensive experiments show that our framework achieves SOTA performance in both image reconstruction and industrial downstream tasks. Our code will be released at \url{https://github.com/jqtangust/FilmRemoval}. |
| title | Learning to Remove Wrinkled Transparent Film with Polarized Prior |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.04368 |