Various low-light image enhancement techniques inevitably introduce noise to varying degrees while improving visibility, leading to a decline in image quality that adversely affects downstream vision tasks. Existing post-processing denoising methods often produce overly smooth results lacking in detail, presenting the challenge of balancing noise suppression and detail preservation. To address this, this paper proposes a conditional diffusion denoising framework based on multi-feature fusion. The framework utilizes a diffusion model to learn the conditional distribution between underexposed and normally exposed images. Complementary features are extracted in parallel through four dedicated branches. These multi-source features are then concatenated and fused to enrich semantic information. Subsequently, redundant information is compressed via 1 × 1 convolutional layers, mitigating the issue of information degradation commonly encountered with U-Net skip connections during multi-scale feature fusion. Experimental results demonstrate the method’s applicability across diverse scenarios and illumination conditions. It outperforms both traditional methods and mainstream deep learning models in qualitative and quantitative evaluations, particularly in terms of perceptual quality. This research provides significant technical support for subsequent image restoration and denoising within low-light enhancement pipelines.