关键词: 关键词: 低光图像增强, 轻量残差特征提取, 亮度感知, 动态特征融合, 轻量化设计, 混合损失函数

Abstract: Abstract: 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 network based on lightweight residual and brightness-aware dynamic feature fusion was proposed. Firstly, a lightweight residual feature extraction module was designed in the encoder stage to mitigate information loss caused by downsampling and improve the extraction capability for low-light features. Secondly, a brightness-aware deep semantic processing module was introduced between the encoder and decoder to strengthen the network’s perception and restoration of brightness distribution. Thirdly, a dynamic feature fusion mechanism was applied in the decoder to enhance the integration of skip-connected and upsampled features, thereby improving noise suppression and detail restoration. Furthermore, a perception-color hybrid loss function was proposed to enhance structural consistency and color fidelity of the enhanced images. Finally, a combination of group convolution and Ghost convolution was used to achieve lightweight network design, balancing enhancement quality and computational efficiency. Experimental results on the LOL (LOw-Light) benchmark datasets (LOL-v1, LOL-v2-real, and LOL-v2-syn) demonstrate that the proposed network achieves peak signal-to-noise ratios (PSNR) of 23.71 dB, 21.46 dB, and 24.80 dB, and structural similarity indices (SSIM) of 0.852, 0.863, and 0.933. The overall network adopts a fully convolutional and lightweight architecture. Compared with the lightweight deep curve estimation method Zero-DCE (Zero-Reference Deep Curve Estimation), the proposed method achieves significantly better enhancement quality. Furthermore, compared with EnGAN (Enlighten Generative Adversarial Network), which is based on attention mechanisms and generative adversarial networks, and LLFormer (Low-Light Transformer), which is based on a Transformer architecture, the proposed network substantially reduces model complexity and inference cost while maintaining high enhancement performance. It effectively balances brightness improvement, noise suppression, detail preservation, structural integrity, and color restoration with computational efficiency.

Key words: low-light image enhancement, lightweight residual feature extraction, brightness-aware, dynamic feature fusion, lightweight network design, hybrid loss function