  The insufficient number of annotated thermal infrared (TIR) image datasets
not only hinders TIR image-based deep learning networks to have comparable
performances to that of RGB but it also limits the supervised learning of TIR
image-based tasks with challenging labels. As a remedy, we propose a modified
multidomain RGB to TIR image translation model focused on edge preservation to
employ annotated RGB images with challenging labels. Our proposed method not
only preserves key details in the original image but also leverages the optimal
TIR style code to portray accurate TIR characteristics in the translated image,
when applied on both synthetic and real world RGB images. Using our translation
model, we have enabled the supervised learning of deep TIR image-based optical
flow estimation and object detection that ameliorated in deep TIR optical flow
estimation by reduction in end point error by 56.5\% on average and the best
object detection mAP of 23.9\% respectively. Our code and supplementary
materials are available at https://github.com/rpmsnu/sRGB-TIR.
