Sep 2026· International Conference on Information Photonics· pp. 1-6· 0 citations· 26 references
TL;DR
A Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images and demonstrates strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics.
Abstract
Low-light image enhancement is crucial in situations where visible sensors might suffer from severe noise and information loss ( e.g., nighttime surveillance). Recent approaches investigate auxiliary modalities invariant to illumination to improve the performance, such as thermal infrared imaging. We propose a Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images. Our method utilizes a two-stage pipeline: first, we employ an intrinsic decomposition strategy to separate re-flectance and shading components through knowledge distillation, where a teacher network guides a student model in re-constructing consistent intrinsic components; then, a refine-ment stage restores fine structures and visual details. We train the proposed model on synthetic data from HDRT dataset and demonstrate strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics. Code is available at : https://github.com/simonemelc/TIRGlow
SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise and a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement.
Yue Zhang, Zhi-Liang Wu, Yuxuan Hou et al.· Proceedings of the Thirty-Fi...· 0 citations
This work proposes a model-driven deep neural network to effectively handle the joint degradation of low light and blur and designs an illumination enhancement module (IEM) and a reflectance refinement module (RRM) to improve brightness, restore fine details, and suppress noise.
Yao Xiao, You-Shen Xia, Zhen-Yu Lu et al.· IEEE Transactions on Neural...· 0 citations
Results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement, IA2former, which effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions.
Tian-Qi Jiang· Poster Volume 0007 The 2026...· 0 citations
The proposed framework provides a robust, scalable solution for real-world illumination enhancement across diverse lighting conditions and consistently outperforms state-of-the-art supervised and unsupervised methods in terms of fidelity, perceptual quality, and generalization.
Yasmin Yasin, Muhammad Usman, Ibrahim Radwan et al.· 0 citations
This work validates the design effectiveness of decoupling global and local representations within a frozen backbone, and establishes a new baseline for parameter-efficient enhancement.
Yanpeng Cao, Yue Wang, Ming-Hui Liang et al.· Pattern Analysis and Applica...· 0 citations
Flow matching enables efficient low-light image enhancement (LLIE) with very few sampling steps, yet standard architectures lack explicit scene understanding, causing structural degradation and artifacts in challenging regions. We propose DINO-guided Flow Matching, which leverages a frozen DINOv3 backbone to provide illumination and structure priors for the Pixel MeanFlows framework. Specifically, we extract dual-layer features—shallow illumination-sensitive features and deep degradation-invariant structure features—and bridge the low-light/normal-light domain gap through a lightweight DINO Feature Corrector (DFC). The corrected features are injected into the flow-matching UNet via Retinex-inspired FiLM modulation and cross-attention, providing spatially adaptive guidance. Furthermore, we identify a systematic brightness drift problem arising from the marginal distribution mismatch between source and target domains, and address it with an Optimal-Transport Look-Up Table (OT-LUT) that pre-aligns the intensity distribution at negligible cost. Experiments on LOL-v2-real, LOL-v2-synthetic, and MIT-5K demonstrate state-of-the-art results in both distortion metrics (PSNR, SSIM) and perceptual quality (LPIPS).
Xiang-Rui Zeng, Ling-Yu Zhu, Jing-Ming He et al.· 2026 International Symposium...· 0 citations
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