Deep Light Pollution Removal in Night Cityscape Photographs
2026-04-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how artificial lights in cities make night photos look blurry and washed out, hiding stars and making halos around lights. They created a new model that better explains how light spreads unevenly and how hidden lights cause skyglow. To train their system, they used a mix of computer-generated and real images to handle the lack of paired examples. Their method helps remove unwanted light effects, making night photos look closer to how the night really appears. Experiments show it works better than earlier ways of fixing night photos.
light pollutionskyglowanisotropic light spreadnighttime dehazingradiative footprintgenerative modelssynthetic-real couplingnight photographyimage restorationcomputer vision
Authors
Hao Wang, Xiaolin Wu, Xi Zhang, Baoqing Sun
Abstract
Nighttime photography is severely degraded by light pollution induced by pervasive artificial lighting in urban environments. After long-range scattering and spatial diffusion, unwanted artificial light overwhelms natural night luminance, generates skyglow that washes out the view of stars and celestial objects and produces halos and glow artifacts around light sources. Unlike nighttime dehazing, which aims to improve detail legibility through thick air, the objective of light pollution removal is to restore the pristine night appearance by neutralizing the radiative footprint of ground lighting. In this paper we introduce a physically-based degradation model that adds to the previous ones for nighttime dehazing two critical aspects; (i) anisotropic spread of directional light sources, and (ii) skyglow caused by invisible surface lights behind skylines. In addition, we construct a training strategy that leverages large generative model and synthetic-real coupling to compensate for the scarcity of paired real data and enhance generalization. Extensive experiments demonstrate that the proposed formulation and learning framework substantially reduce light pollution artifacts and better recover authentic night imagery than prior nighttime restoration methods.