  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.
