  Infrared cameras are often utilized to enhance the night vision since the
visible light cameras exhibit inferior efficacy without sufficient
illumination. However, infrared data possesses inadequate color contrast and
representation ability attributed to its intrinsic heat-related imaging
principle. This makes it arduous to capture and analyze information for human
beings, meanwhile hindering its application. Although, the domain gaps between
unpaired nighttime infrared and daytime visible videos are even huger than
paired ones that captured at the same time, establishing an effective
translation mapping will greatly contribute to various fields. In this case,
the structural knowledge within nighttime infrared videos and semantic
information contained in the translated daytime visible pairs could be utilized
simultaneously. To this end, we propose a tailored framework ROMA that couples
with our introduced cRoss-domain regiOn siMilarity mAtching technique for
bridging the huge gaps. To be specific, ROMA could efficiently translate the
unpaired nighttime infrared videos into fine-grained daytime visible ones,
meanwhile maintain the spatiotemporal consistency via matching the cross-domain
region similarity. Furthermore, we design a multiscale region-wise
discriminator to distinguish the details from synthesized visible results and
real references. Extensive experiments and evaluations for specific
applications indicate ROMA outperforms the state-of-the-art methods. Moreover,
we provide a new and challenging dataset encouraging further research for
unpaired nighttime infrared and daytime visible video translation, named
InfraredCity. In particular, it consists of 9 long video clips including City,
Highway and Monitor scenarios. All clips could be split into 603,142 frames in
total, which are 20 times larger than the recently released daytime
infrared-to-visible dataset IRVI.
