LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge
Junlong Ma, Conghan Yue, Zhengwei Peng, Dongyu Zhang
Abstract
This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://github.com/M-Chase/LLDB
BibTeX
@inproceedings{icassp2025_lldbefficientlow,
title = {LLDB: Efficient Low-Light Image Enhancement with Difffusion Bridge},
author = {Junlong Ma and Conghan Yue and Zhengwei Peng and Dongyu Zhang},
booktitle = {ICASSP 2025},
year = {2025}
}