Low-light video enhancement is highly demanding in maintaining spatiotemporal color consistency. Therefore, improving the accuracy of color mapping and keeping the latency low is challenging. Based on this, we propose incorporating Wavelet-priori for 4D Lookup Table (WaveLUT), which effectively enhances the color coherence between video frames and the accuracy of color mapping while maintaining low latency. Specifically, we use the wavelet low-frequency domain to construct an optimized lookup prior and achieve an adaptive enhancement effect through a designed Wavelet-prior 4D lookup table. To effectively compensate the a priori loss in the low light region, we further explore a dynamic fusion strategy that adaptively determines the spatial weights based on the correlation between the wavelet lighting prior and the target intensity structure. In addition, during the training phase, we devise a text-driven appearance reconstruction method that dynamically balances brightness and content through multimodal semantics-driven Fourier spectra. Extensive experiments on a wide range of benchmark datasets show that this method effectively enhances the previous method's ability to perceive the color space and achieves metric-favorable and perceptually oriented real-time enhancement while maintaining high efficiency.

翻译：低光视频增强在保持时空颜色一致性方面要求极高。因此，提高颜色映射精度并维持低延迟具有挑战性。基于此，我们提出将小波先验融入4D查找表（WaveLUT），该方法在保持低延迟的同时，有效增强了视频帧间的颜色连贯性与颜色映射精度。具体而言，我们利用小波低频域构建优化的查找先验，并通过设计的小波先验4D查找表实现自适应增强效果。为有效补偿低光区域的先验损失，我们进一步探索了一种动态融合策略，该策略基于小波光照先验与目标强度结构之间的相关性自适应确定空间权重。此外，在训练阶段，我们设计了一种文本驱动的外观重建方法，通过多模态语义驱动的傅里叶频谱动态平衡亮度与内容。在多种基准数据集上的大量实验表明，该方法有效提升了先前方法对颜色空间的感知能力，并在保持高效率的同时，实现了指标优异且感知导向的实时增强。