Illumination prior guided low-light image enhancement method

ZHANG Kaibing; REN Kexin; LI Yingjian; MENG Yalei; LI Pengfang

doi:10.16152/j.cnki.xdxbzr.2026-03-007

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Illumination prior guided low-light image enhancement method

更新时间：2026-07-07

- Illumination prior guided low-light image enhancement method
- Journal of Northwest University (Natural Science Edition) Vol. 56, Issue 3, Pages: 538-550(2026)
- 作者机构：
  
  1.西安工程大学　计算机科学学院，陕西　西安　710048
  2.陕西省服装设计智能化重点实验室，陕西　西安　710048
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2026-03-007
  CLC： TP391.41
- Received：26 December 2025，
  
  Revised：2026-01-08，
  
  Published：25 June 2026
- 稿件说明：
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ZHANG Kaibing, REN Kexin, LI Yingjian, et al. Illumination prior guided low-light image enhancement method[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 538-550.
DOI：

ZHANG Kaibing, REN Kexin, LI Yingjian, et al. Illumination prior guided low-light image enhancement method[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 538-550. DOI： 10.16152/j.cnki.xdxbzr.2026-03-007.

摘要

针对现有低照度图像增强方法存在光照适应性差、全局结构与局部细节难以平衡的问题，提出一种光照先验引导的低照度图像增强方法（light-prior driven low-light enhancement network，LPD-Net）。首先，基于Retinex理论构建光照先验估计模块（illumination prior module，IPM），通过引入同态滤波先验生成光照归一化特征，实现对非均匀光照的全局校正。其次，构建基于编码器解码器架构的Mamba优化增强模块（Mamba enhanced module，MEM），采用分层建模策略，构建由局部窗口Mamba与全局Mamba组成的多尺度路径，实现局部细节增强与全局亮度恢复。在此基础上，引入双流融合色彩调制模块（dual-stream fusioncolor modulation，DFCM），通过双向卷积块获取横纵空间依赖关系，并结合轻量注意力机制实现全局局部特征的融合。同时，利用通道门控与色彩调制机制自适应恢复色彩信息与局部图像细节。最后，通过引入总变差正则化对光照图进行约束，防止因过度增强而导致的图像结构一致性问题。实验结果表明，本文方法在LOLv1数据集上取得了24.370 dB的PSNR与0.853的SSIM，性能明显优于现有方法。该方法在光照校正、噪声抑制和细节保持等方面均展现出显著优势，同时对实际低照度场景也具有良好的泛化能力。

Abstract

Aiming at the shortcomings of existing low-light image enhancement methods

such as poor illumination adaptability and difficulty in balancing global structure and local details

this paper proposes a light-prior driven low-light enhancement network (LPD-Net). First

an illumination prior module (IPM) is constructed based on Retinex theory

which generates illumination-normalized features by introducing homomorphic filtering priors to achieve global correction of non-uniform illumination. Second

a Mamba enhanced module (MEM) based on an encoder-decoder architecture is designed

employing a hierarchical modeling strategy and constructing a multi-scale pathway consisting of local window Mamba and global Mamba to achieve local detail enhancement and global brightness restoration. On this basis

a dual-stream fusion color modulation (DFCM) module is introduced

which captures horizontal and vertical spatial dependencies through bidirectional convolutional blocks and integrates global-local features using a lightweight attention mechanism. Simultaneously

channel gating and color modulation mechanisms are applied to adaptively restore color information and fine image details. Finally

total variation regularization is introduced to constrain the illumination map

preventing structural inconsistency caused by over-enhancement. Experimental results show that the proposed method achieves 24.370 dB PSNR and 0.853 SSIM on the LOLv1 dataset

significantly outperforming existing methods. The method demonstrates notable advantages in illumination correction

noise suppression

and detail preservation

while also exhibiting strong generalization capability for real-world low-light scenarios.

关键词

Keywords

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