Low-light images often suffer from insufficient brightness， severe noise， detail loss， and color distortion， which significantly degrade visual quality and hinder the performance of subsequent vision tasks. To address these issues， a Low-Light Image Enhancement （LLIE） Network based on Lightweight Residual and Brightness-aware Dynamic feature fUsion （LRBDU-Net） was proposed. Firstly， a Lightweight Residual Feature Extraction （LRFE） module was designed in the encoding stage to mitigate information loss caused by downsampling and improve the extraction capability for low-light features. Secondly， a Brightness-aware Deep Semantic feature Processing （BDSP） module was designed in the encoding and decoding transition stage to strengthen the network’s perception and restoration abilities of brightness distribution of low-light images. Thirdly， a lightweight Dynamic Feature Fusion （DFF） mechanism was applied in the decoding stage to enhance the fusion effect of skip-connected and upsampled features， thereby improving network’s noise suppression and detail restoration abilities of low-light images. Fourthly， a Perception-Color Hybrid loss function （PCH） was proposed to further enhance structural consistency and color reproduction degree of LLIE. Finally， a combined structure of Group convolution and Ghost convolution （GpGh） was used to perform lightweight network design， thereby ensuring quality of LLIE and improving computational efficiency at the same time. Experimental results on the LOL （LOw-Light） datasets （LOL-v1， LOL-v2-real， and LOL-v2-syn） demonstrate that the proposed network achieves the Peak Signal-to-Noise Ratio （PSNR） of 23.71 dB， 21.46 dB， and 24.80 dB， respectively， and the Structural SIMilarity index （SSIM） of 0.852， 0.863， and 0.933， respectively. Overall， this network adopts pure convolutional architecture and lightweight design. Compared with the lightweight deep curve estimation method — Zero-reference Deep Curve Estimation （Zero-DCE） network， this network achieves significantly better quality of LLIE； compared with LLIE Generative Adversarial Network based on attention mechanism — EnGAN （Enlighten Generative Adversarial Network）， and LLIE method based on Transformer — LLFormer （Low-Light Transformer）， this network reduces model complexity and inference calculation cost significantly while maintaining high LLIE performance. It can be seen that the proposed network balances LLIE performance such as brightness improvement， noise suppression， detail restoration， structural integrity， and color reproduction degree with network computational efficiency well.