  High dynamic range (HDR) video reconstruction from sequences captured with
alternating exposures is a very challenging problem. Existing methods often
align low dynamic range (LDR) input sequence in the image space using optical
flow, and then merge the aligned images to produce HDR output. However,
accurate alignment and fusion in the image space are difficult due to the
missing details in the over-exposed regions and noise in the under-exposed
regions, resulting in unpleasing ghosting artifacts. To enable more accurate
alignment and HDR fusion, we introduce a coarse-to-fine deep learning framework
for HDR video reconstruction. Firstly, we perform coarse alignment and pixel
blending in the image space to estimate the coarse HDR video. Secondly, we
conduct more sophisticated alignment and temporal fusion in the feature space
of the coarse HDR video to produce better reconstruction. Considering the fact
that there is no publicly available dataset for quantitative and comprehensive
evaluation of HDR video reconstruction methods, we collect such a benchmark
dataset, which contains $97$ sequences of static scenes and 184 testing pairs
of dynamic scenes. Extensive experiments show that our method outperforms
previous state-of-the-art methods. Our dataset, code and model will be made
publicly available.
