Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot

• URL: http://arxiv.org/abs/2111.07281v1
• Date: Sun, 14 Nov 2021 08:54:26 GMT
• Title: Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot
• Authors: Yilun Xu, Ziyang Liu, Xingming Wu, Weihai Chen, Changyun Wen and Zhengguo Li
• Abstract summary: It is challenging to restore a full-resolution HDR image from a real-world image with SVE. A spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. An exposure-guidance method is proposed against the interference from over- and under-exposed pixels.
• Score: 30.483754080108444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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