  It is very challenging for various visual tasks such as image fusion,
pedestrian detection and image-to-image translation in low light conditions due
to the loss of effective target areas. In this case, infrared and visible
images can be used together to provide both rich detail information and
effective target areas. In this paper, we present LLVIP, a visible-infrared
paired dataset for low-light vision. This dataset contains 30976 images, or
15488 pairs, most of which were taken at very dark scenes, and all of the
images are strictly aligned in time and space. Pedestrians in the dataset are
labeled. We compare the dataset with other visible-infrared datasets and
evaluate the performance of some popular visual algorithms including image
fusion, pedestrian detection and image-to-image translation on the dataset. The
experimental results demonstrate the complementary effect of fusion on image
information, and find the deficiency of existing algorithms of the three visual
tasks in very low-light conditions. We believe the LLVIP dataset will
contribute to the community of computer vision by promoting image fusion,
pedestrian detection and image-to-image translation in very low-light
applications. The dataset is being released in
https://bupt-ai-cz.github.io/LLVIP. Raw data is also provided for further
research such as image registration.
