  Deep high dynamic range (HDR) imaging as an image translation issue has
achieved great performance without explicit optical flow alignment. However,
challenges remain over content association ambiguities especially caused by
saturation and large-scale movements. To address the ghosting issue and enhance
the details in saturated regions, we propose a scale-aware two-stage high
dynamic range imaging framework (STHDR) to generate high-quality ghost-free HDR
image. The scale-aware technique and two-stage fusion strategy can
progressively and effectively improve the HDR composition performance.
Specifically, our framework consists of feature alignment and two-stage fusion.
In feature alignment, we propose a spatial correct module (SCM) to better
exploit useful information among non-aligned features to avoid ghosting and
saturation. In the first stage of feature fusion, we obtain a preliminary
fusion result with little ghosting. In the second stage, we conflate the
results of the first stage with aligned features to further reduce residual
artifacts and thus improve the overall quality. Extensive experimental results
on the typical test dataset validate the effectiveness of the proposed STHDR in
terms of speed and quality.
