  Benefitting from insensitivity to light and high penetration of foggy
environments, infrared cameras are widely used for sensing in nighttime traffic
scenes. However, the low contrast and lack of chromaticity of thermal infrared
(TIR) images hinder the human interpretation and portability of high-level
computer vision algorithms. Colorization to translate a nighttime TIR image
into a daytime color (NTIR2DC) image may be a promising way to facilitate
nighttime scene perception. Despite recent impressive advances in image
translation, semantic encoding entanglement and geometric distortion in the
NTIR2DC task remain under-addressed. Hence, we propose a toP-down attEntion And
gRadient aLignment based GAN, referred to as PearlGAN. A top-down guided
attention module and an elaborate attentional loss are first designed to reduce
the semantic encoding ambiguity during translation. Then, a structured gradient
alignment loss is introduced to encourage edge consistency between the
translated and input images. In addition, pixel-level annotation is carried out
on a subset of FLIR and KAIST datasets to evaluate the semantic preservation
performance of multiple translation methods. Furthermore, a new metric is
devised to evaluate the geometric consistency in the translation process.
Extensive experiments demonstrate the superiority of the proposed PearlGAN over
other image translation methods for the NTIR2DC task. The source code and
labeled segmentation masks will be available at
\url{https://github.com/FuyaLuo/PearlGAN/}.
