Deep Joint Demosaicing and High Dynamic Range Imaging Within a Single Shot
| العنوان: | Deep Joint Demosaicing and High Dynamic Range Imaging Within a Single Shot |
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| المؤلفون: | Changyun Wen, Zhengguo Li, Xingming Wu, Yilun Xu, Weihai Chen, Ziyang Liu |
| المساهمون: | School of Electrical and Electronic Engineering |
| المصدر: | IEEE Transactions on Circuits and Systems for Video Technology. 32:4255-4270 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Bayer filter, Demosaicing, Pixel, business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Electrical Engineering and Systems Science - Image and Video Processing, Convolution, HDRi, Interference (communication), High-dynamic-range imaging, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and electronic engineering [Engineering], Media Technology, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, Ghosting, business, Spatially Varying Exposure |
| الوصف: | Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVE-based HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over- and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over- and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-to-end single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods. 15 pages, 17 figures |
| تدمد: | 1558-2205 1051-8215 |
| DOI: | 10.1109/tcsvt.2021.3129691 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69b18532c6e86ae6dd9d8bff3518c617 https://doi.org/10.1109/tcsvt.2021.3129691 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....69b18532c6e86ae6dd9d8bff3518c617 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15582205 10518215 |
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| DOI: | 10.1109/tcsvt.2021.3129691 |