Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori
This addresses the problem of maintaining spatiotemporal color consistency in low-light videos for applications requiring real-time processing, representing an incremental improvement over previous methods.
The paper tackled low-light video enhancement by proposing WaveLUT, which incorporates wavelet priors into a 4D lookup table to improve color coherence and mapping accuracy while maintaining low latency, achieving metric-favorable and perceptually oriented real-time enhancement.
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.