Novel view synthesis from low-light, noisy, and motion-blurred imagery remains a valuable and challenging task. Current volumetric rendering methods struggle with compound degradation, and sequential 2D preprocessing introduces artifacts due to interdependencies. In this work, we introduce FLED-GS, a fast low-light enhancement and deblurring framework that reformulates 3D scene restoration as an alternating cycle of enhancement and reconstruction. Specifically, FLED-GS inserts several intermediate brightness anchors to enable progressive recovery, preventing noise blow-up from harming deblurring or geometry. Each iteration sharpens inputs with an off-the-shelf 2D deblurrer and then performs noise-aware 3DGS reconstruction that estimates and suppresses noise while producing clean priors for the next level. Experiments show FLED-GS outperforms state-of-the-art LuSh-NeRF, achieving 21× faster training and 11× faster rendering.
@article{arxiv.2603.08133,
title = {Fast Low-light Enhancement and Deblurring for 3D Dark Scenes},
author = {Feng Zhang and Jinglong Wang and Ze Li and Yanghong Zhou and Yang Chen and Lei Chen and Xiatian Zhu},
journal= {arXiv preprint arXiv:2603.08133},
year = {2026}
}