Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori

TL;DR

This paper introduces WaveLUT, a wavelet-priori based 4D lookup table method that enhances low-light video quality by improving color accuracy and coherence while ensuring low latency and real-time performance.

Contribution

It proposes a novel Wavelet-priori 4D lookup table and a dynamic fusion strategy for adaptive low-light video enhancement, integrating multimodal semantics-driven Fourier spectra for better results.

Findings

Effective color space perception improvement

Achieves real-time enhancement with high efficiency

Outperforms previous methods on benchmark datasets

Abstract

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.