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Benefitting from insensitivity to light and high penetration of foggyenvironments, infrared cameras are widely used for sensing in nighttime trafficscenes. However, the low contrast and lack of chromaticity of thermal infrared(TIR) images hinder the human interpretation and portability of high-levelcomputer vision algorithms. Colorization to translate a nighttime TIR imageinto a daytime color (NTIR2DC) image may be a promising way to facilitatenighttime scene perception. Despite recent impressive advances in imagetranslation, semantic encoding entanglement and geometric distortion in theNTIR2DC task remain under-addressed. Hence, we propose a toP-down attEntion AndgRadient aLignment based GAN, referred to as PearlGAN. A top-down guidedattention module and an elaborate attentional loss are first designed to reducethe semantic encoding ambiguity during translation. Then, a structured gradientalignment loss is introduced to encourage edge consistency between thetranslated and input images. In addition, pixel-level annotation is carried outon a subset of FLIR and KAIST datasets to evaluate the semantic preservationperformance of multiple translation methods. Furthermore, a new metric isdevised to evaluate the geometric consistency in the translation process.Extensive experiments demonstrate the superiority of the proposed PearlGAN overother image translation methods for the NTIR2DC task. The source code andlabeled segmentation masks will be available at\url{https://github.com/FuyaLuo/PearlGAN/}.

Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention