FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware
  Lookup Table
Wenhao Li Guangyang Wu Wenyi Wang Peiran Ren Xiaohong Liu
Low-Light Video Enhancement (LLVE) has received considerable attention in
recent years. One of the critical requirements of LLVE is inter-frame
brightness consistency, which is essential for maintaining the temporal
coherence of the enhanced video. However, most existing single-image-based
methods fail to address this issue, resulting in flickering effect that
degrades the overall quality after enhancement. Moreover, 3D Convolution Neural
Network (CNN)-based methods, which are designed for video to maintain
inter-frame consistency, are computationally expensive, making them impractical
for real-time applications. To address these issues, we propose an efficient
pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to
maintain inter-frame brightness consistency effectively. Specifically, we
design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive
enhancement, which addresses the low-dynamic problem in low-light scenarios.
This enables FastLLVE to perform low-latency and low-complexity enhancement
operations while maintaining high-quality results. Experimental results on
benchmark datasets demonstrate that our method achieves the State-Of-The-Art
(SOTA) performance in terms of both image quality and inter-frame brightness
consistency. More importantly, our FastLLVE can process 1,080p videos at
$\mathit{50+}$ Frames Per Second (FPS), which is $\mathit{2 \times}$ faster
than SOTA CNN-based methods in inference time, making it a promising solution
for real-time applications. The code is available at
https://github.com/Wenhao-Li-777/FastLLVE.