Single Image Reflection Removal with Patch Reflectance Prior
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910226644467712 |
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| author | Han, Dongshen Yoon, Heechan Kwon, Hyukmin Kim, Hyun-Cheol Choo, Hyon-Gon Lee, Seungkyu Zhang, Chaoning |
| author_facet | Han, Dongshen Yoon, Heechan Kwon, Hyukmin Kim, Hyun-Cheol Choo, Hyon-Gon Lee, Seungkyu Zhang, Chaoning |
| contents | Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and reflection. Many existing methods rely on specific prior assumptions to resolve the problem. In this paper, we propose a general reflection intensity prior that captures the intensity of the reflection phenomenon and demonstrate its effectiveness. To learn the reflection intensity prior, we introduce the Reflection Prior Extraction Network (RPEN). By segmenting images into regional patches, RPEN learns non-uniform reflection prior in an image. We propose Prior-based Reflection Removal Network (PRRN) using a simple transformer U-Net architecture that adapts reflection prior fed from RPEN. Experimental results on real-world benchmarks demonstrate the effectiveness of our approach achieving state-of-the-art accuracy in SIRR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03798 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Single Image Reflection Removal with Patch Reflectance Prior Han, Dongshen Yoon, Heechan Kwon, Hyukmin Kim, Hyun-Cheol Choo, Hyon-Gon Lee, Seungkyu Zhang, Chaoning Computer Vision and Pattern Recognition Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and reflection. Many existing methods rely on specific prior assumptions to resolve the problem. In this paper, we propose a general reflection intensity prior that captures the intensity of the reflection phenomenon and demonstrate its effectiveness. To learn the reflection intensity prior, we introduce the Reflection Prior Extraction Network (RPEN). By segmenting images into regional patches, RPEN learns non-uniform reflection prior in an image. We propose Prior-based Reflection Removal Network (PRRN) using a simple transformer U-Net architecture that adapts reflection prior fed from RPEN. Experimental results on real-world benchmarks demonstrate the effectiveness of our approach achieving state-of-the-art accuracy in SIRR. |
| title | Single Image Reflection Removal with Patch Reflectance Prior |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.03798 |