Fast Low-light Enhancement and Deblurring for 3D Dark Scenes

TL;DR

FLED-GS is a fast, iterative framework that enhances and deblurs low-light, noisy, and blurred 3D scenes, significantly improving quality and speed over existing methods.

Contribution

The paper introduces FLED-GS, a novel 3D scene restoration method that combines progressive enhancement and reconstruction with intermediate brightness anchors.

Findings

Outperforms state-of-the-art LuSh-NeRF in quality.

Achieves 21× faster training.

Achieves 11× faster rendering.

Abstract

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$\times$ faster training and 11$\times$ faster rendering.