  Due to hardware constraints, standard off-the-shelf digital cameras suffers
from low dynamic range (LDR) and low frame per second (FPS) outputs. Previous
works in high dynamic range (HDR) video reconstruction uses sequence of
alternating exposure LDR frames as input, and align the neighbouring frames
using optical flow based networks. However, these methods often result in
motion artifacts in challenging situations. This is because, the alternate
exposure frames have to be exposure matched in order to apply alignment using
optical flow. Hence, over-saturation and noise in the LDR frames results in
inaccurate alignment. To this end, we propose to align the input LDR frames
using a pre-trained video frame interpolation network. This results in better
alignment of LDR frames, since we circumvent the error-prone exposure matching
step, and directly generate intermediate missing frames from the same exposure
inputs. Furthermore, it allows us to generate high FPS HDR videos by
recursively interpolating the intermediate frames. Through this work, we
propose to use video frame interpolation for HDR video reconstruction, and
present the first method to generate high FPS HDR videos. Experimental results
demonstrate the efficacy of the proposed framework against optical flow based
alignment methods, with an absolute improvement of 2.4 PSNR value on standard
HDR video datasets [1], [2] and further benchmark our method for high FPS HDR
video generation.
