  Ghosting artifacts, motion blur, and low fidelity in highlight are the main
challenges in High Dynamic Range (HDR) imaging from multiple Low Dynamic Range
(LDR) images. These issues come from using the medium-exposed image as the
reference frame in previous methods. To deal with them, we propose to use the
under-exposed image as the reference to avoid these issues. However, the heavy
noise in dark regions of the under-exposed image becomes a new problem.
Therefore, we propose a joint HDR and denoising pipeline, containing two
sub-networks: (i) a pre-denoising network (PreDNNet) to adaptively denoise
input LDRs by exploiting exposure priors; (ii) a pyramid cascading fusion
network (PCFNet), introducing an attention mechanism and cascading structure in
a multi-scale manner. To further leverage these two paradigms, we propose a
selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing
scenario-specific priors to conduct the path selection with an accuracy of more
than 93.3$\%$. We create the first joint HDR and denoising benchmark dataset,
which contains a variety of challenging HDR and denoising scenes and supports
the switching of the reference image. Extensive experiment results show that
our method achieves superior performance to previous methods.
