HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917586854215680 |
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| author | Barua, Hrishav Bakul Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Stefanov, Kalin |
| author_facet | Barua, Hrishav Bakul Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Stefanov, Kalin |
| contents | High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are over- or under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation Barua, Hrishav Bakul Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Stefanov, Kalin Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Graphics Machine Learning Multimedia Artificial intelligence, Computer vision, Machine learning, Deep learning I.3.3; I.4.5 High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are over- or under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods. |
| title | HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Graphics Machine Learning Multimedia Artificial intelligence, Computer vision, Machine learning, Deep learning I.3.3; I.4.5 |
| url | https://arxiv.org/abs/2402.06692 |