Xu, Y., Liu, Z., Wu, X., Chen, W., Wen, C., & Li, Z. (2022). 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://doi.org/10.1109/tcsvt.2021.3129691
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 surpasses the related state-of-the-art methods, and the problem of cumulative errors is solved.
License type:
Publisher Copyright
Funding Info:
This work is supported by the National Natural Science Foundation of China under the research project 61620106012.