MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement
Chen Wu Zhuoran Zheng Xiuyi Jia Wenqi Ren
With the continuous advancement of imaging devices, the prevalence of
Ultra-High-Definition (UHD) images is rising. Although many image restoration
methods have achieved promising results, they are not directly applicable to
UHD images on devices with limited computational resources due to the
inherently high computational complexity of UHD images. In this paper, we focus
on the task of low-light image enhancement (LLIE) and propose a novel LLIE
method called MixNet, which is designed explicitly for UHD images. To capture
the long-range dependency of features without introducing excessive
computational complexity, we present the Global Feature Modulation Layer
(GFML). GFML associates features from different views by permuting the feature
maps, enabling efficient modeling of long-range dependency. In addition, we
also design the Local Feature Modulation Layer (LFML) and Feed-forward Layer
(FFL) to capture local features and transform features into a compact
representation. This way, our MixNet achieves effective LLIE with few model
parameters and low computational complexity. We conducted extensive experiments
on both synthetic and real-world datasets, and the comprehensive results
demonstrate that our proposed method surpasses the performance of current
state-of-the-art methods. The code will be available at
\url{https://github.com/zzr-idam/MixNet}.