Jul 2026· 2026 International Symposium on Machine Learning and Media Computing (MLMC)· pp. 1-6· 0 citations· 36 references
Abstract
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).
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
Low-light image enhancement aims to improve image quality under insufficient illumination while recovering underlying structural and texture information. Existing approaches, ranging from conventional image processing techniques to deep generative models, have shown promising performance in brightness enhancement and d...
Yao Lu, Jun-Liang Tan, Hai-Peng Liang et al.· Journal of King Saud Univers...· 0 citations
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or unnatural enhancements when deployed in real-world applications. A key challenge...
Zhi-Hua Wang, Qing-Hua Lin, Fei-Yang Liu et al.· IEEE Transactions on Image P...· 0 citations
DARD first extracts image-specific physical priors from the degraded input through a test-time degradation-aware Retinex decomposition, thereby providing reliable structural guidance for zero-shot restoration and injects these priors into reverse diffusion through a timestep-adaptive frequency fusion strategy to balanc...
Wen-Jie Cai, Yuezhe Yang, Jian-Yang Xia et al.· IEEE transactions on circuit...· 0 citations
The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone, and uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions.
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
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