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|Title:||Deep joint demosaicing and high dynamic range imaging within a single shot||Authors:||Xu, Yilun
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2021||Source:||Xu, Y., Liu, Z., Wu, X., Chen, W., Wen, C. & Li, Z. (2021). Deep joint demosaicing and high dynamic range imaging within a single shot. IEEE Transactions On Circuits and Systems for Video Technology, 32(7), 4255-4270. https://dx.doi.org/10.1109/TCSVT.2021.3129691||Journal:||IEEE Transactions on Circuits and Systems for Video Technology||Abstract:||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. Related codes and datasets will be provided at https://github.com/yilun-xu/SVEHDRI/.||URI:||https://hdl.handle.net/10356/162751||ISSN:||1051-8215||DOI:||10.1109/TCSVT.2021.3129691||Rights:||© 2021 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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