  High dynamic range (HDR) imaging is of fundamental importance in modern
digital photography pipelines and used to produce a high-quality photograph
with well exposed regions despite varying illumination across the image. This
is typically achieved by merging multiple low dynamic range (LDR) images taken
at different exposures. However, over-exposed regions and misalignment errors
due to poorly compensated motion result in artefacts such as ghosting. In this
paper, we present a new HDR imaging technique that specifically models
alignment and exposure uncertainties to produce high quality HDR results. We
introduce a strategy that learns to jointly align and assess the alignment and
exposure reliability using an HDR-aware, uncertainty-driven attention map that
robustly merges the frames into a single high quality HDR image. Further, we
introduce a progressive, multi-stage image fusion approach that can flexibly
merge any number of LDR images in a permutation-invariant manner. Experimental
results show our method can produce better quality HDR images with up to 1.1dB
PSNR improvement to the state-of-the-art, and subjective improvements in terms
of better detail, colours, and fewer artefacts.
