Low Light Enhancement in Street Scenes Based on Diffusion Model

Rui Xia, Lisong Wang, Taili Li, Pingping Shi

Abstract: Street view images constitute an important part of urban computing, providing data support for tasks such as autonomous driving and landscape planning, and promoting the interaction and collaboration between machines and the urban environment. However, in current practice, the usability of street view images is hindered by low-light conditions, and existing low-light enhancement methods often overlook the high-frequency characteristics specific to street views. Therefore, this paper proposes a conditional diffusion model called SVBoost that incorporates high-frequency information and color balance to achieve targeted enhancement of street view images. The proposed model demonstrates favorable performance in terms of image quality, and the enhancement effect observed in semantic segmentation tasks suggests the potential of this method for downstream applications.

External IDs:dblp:conf/cscwd/XiaWLS24a