  We propose a novel high dynamic range (HDR) video reconstruction method with
new tri-exposure quad-bayer sensors. Thanks to the larger number of exposure
sets and their spatially uniform deployment over a frame, they are more robust
to noise and spatial artifacts than previous spatially varying exposure (SVE)
HDR video methods. Nonetheless, the motion blur from longer exposures, the
noise from short exposures, and inherent spatial artifacts of the SVE methods
remain huge obstacles. Additionally, temporal coherence must be taken into
account for the stability of video reconstruction. To tackle these challenges,
we introduce a novel network architecture that divides-and-conquers these
problems. In order to better adapt the network to the large dynamic range, we
also propose LDR-reconstruction loss that takes equal contributions from both
the highlighted and the shaded pixels of HDR frames. Through a series of
comparisons and ablation studies, we show that the tri-exposure quad-bayer with
our solution is more optimal to capture than previous reconstruction methods,
particularly for the scenes with larger dynamic range and objects with motion.
