Making the Earth Clear at Night: A High-Resolution Nighttime Light Image Deblooming Network

High-resolution nighttime light (HR-NTL) imagery is a reliable indicator of human activity in urban areas at night. However, HR-NTL images on Earth are blurred because of issues such as blooming, noise, and overexposure in the imaging system and transmission path. Toward this end, a nighttime light image deblooming network (NTL-Db-Net) is proposed, which aims to make the Earth clearer at night. The NTL-Db-Net employs a generative adversarial network (GAN) as its deblooming framework. As the lack of ideal clear NTL data for GAN training, our method integrates a blur transfer algorithm to simulate degradation for generating training data. This method was tested using HR-NTL data on Wuhan, Macau, Changchun, Xiamen, Atlanta, and Jerusalem from the HR Jilin-1 satellite constellation. Compared with the results of state-of-the-art self-adjusting model (SEAM), Uformer, and DRBNet, those from NTL-Db-Net demonstrated superior visual features and achieved the highest scores across all selected nonreference evaluation metrics. NTL-Db-Net also exhibited a lower number of network parameters and a smaller time cost. In addition, this study conducted a spatial clustering analysis of nighttime light with road networks and permeable surfaces. The results indicated that nightlights and roads exhibited a distinct spatially aggregated distribution, while permeable surfaces displayed a clear spatially dispersed distribution. The code and data are available at
https://github.com/lixinghua5540/NTL-Db-Net
.