Paper Publications
Release time: 2026-03-31Hits:
- DOI number:10.1109/JSEN.2025.3534566
- Journal:IEEE Sensors Journal
- Key Words:Diffusion probabilistic model (DPM) generative model low-light image enhancement (LLIE) nighttime vehicle recognition
- Abstract:Due to the inherent limitations of camera sensors in capturing adequate light under low-light conditions, images often suffer from various degradation issues, such as illumination imbalances, artifacts, and noise. While generative model-based methods have made remarkable progress in low-light image enhancement (LLIE), they still face challenges such as unstable training and inconsistent generation quality. To address these challenges, we introduce MCLLDiff, a novel multiconditional LLIE method based on diffusion probabilistic model (DPM). MCLL-Diff retains the forward process of DPM but introduces a unique multiconditional noise predictor (MCNP) in the reverse process. We first propose a learnable operator module (LOM) to enrich the prior knowledge incorporated in the reverse process. Then, we use MCNP to effectively integrate prior knowledge, lowlight images, intermediate variables, and time steps to accurately predict noise. To validate the effectiveness of MCLL-Diff in high-level computer vision tasks, we construct a large-scale nighttime vehicle model (NVM) dataset from real-world nighttime street scenarios. Extensive experiments on benchmark datasets demonstrate MCLL-Diff’s superiority in both generalization performance and visual quality. Specifically, we achieved a significant improvement of 0.1 dB in peak signal-to-noise ratio (PSNR) metric on the VE-LOL dataset, and a notable increase of 0.76% in Top-1 accuracy when applied to object recognition on the NVM dataset.
- Indexed by:Journal paper
- Document Type:J
- Volume:25(6):
- Issue:6
- Page Number:9912-9924
- Translation or Not:no
- Date of Publication:2026-03-31
- Included Journals:SCI
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