Abstract
In low-light environments like nighttime driving, image degradation severely challenges in-vehicle camera safety. Since existing enhancement algorithms are often too computationally intensive for vehicular applications, we propose UltraFast-LieNET, a lightweight multi-scale shifted convolutional network for real-time low-light image enhancement. We introduce a Dynamic Shifted Convolution (DSConv) kernel with only 12 learnable parameters for efficient feature extraction. By integrating DSConv with varying shift distances, a Multi-scale Shifted Residual Block (MSRB) is constructed to significantly expand the receptive field. To mitigate lightweight network instability, a residual structure and a novel multi-level gradient-aware loss function are incorporated. UltraFast-LieNET allows flexible parameter configuration, with a minimum size of only 36 parameters. Results on the LOLI-Street dataset show a PSNR of 26.51 dB, outperforming state-of-the-art methods by 4.6 dB while utilizing only 180 parameters. Experiments across four benchmark datasets validate its superior balance of real-time performance and enhancement quality under limited resources. Code is available at https://githubhttps://github.com/YuhanChen2024/UltraFast-LiNET
Abstract (translated)
在低光环境（如夜间驾驶）中，图像质量的下降严重挑战了车载摄像头的安全性。由于现有的增强算法通常对车辆应用来说计算量过大，我们提出了一种名为UltraFast-LieNET的轻量化多尺度移位卷积网络，用于实现实时低光照下的图像增强。我们引入了一个仅包含12个可学习参数的动态移位卷积（DSConv）核，以实现高效的特征提取。通过将具有不同偏移距离的DSConv集成在一起，构建了多尺度移位残差块（MSRB），从而显著扩大感受野范围。为了缓解轻量级网络不稳定的问题，我们还引入了一个残差结构和一种新颖的多层次梯度感知损失函数。 UltraFast-LieNET允许灵活配置参数，最小规模仅为36个参数。在LOLI-Street数据集上的实验结果表明，在仅使用180个参数的情况下，PSNR达到了26.51 dB，超越了最先进的方法4.6 dB。跨四个基准数据集的实验验证了它在有限资源下的实时性能与增强质量之间的出色平衡。 代码可以在GitHub上找到：https://github.com/YuhanChen2024/UltraFast-LiNET
URL
https://arxiv.org/abs/2512.02965
