  Recently, deep learning-based image enhancement algorithms achieved
state-of-the-art (SOTA) performance on several publicly available datasets.
However, most existing methods fail to meet practical requirements either for
visual perception or for computation efficiency, especially for high-resolution
images. In this paper, we propose a novel real-time image enhancer via
learnable spatial-aware 3-dimentional lookup tables(3D LUTs), which well
considers global scenario and local spatial information. Specifically, we
introduce a light weight two-head weight predictor that has two outputs. One is
a 1D weight vector used for image-level scenario adaptation, the other is a 3D
weight map aimed for pixel-wise category fusion. We learn the spatial-aware 3D
LUTs and fuse them according to the aforementioned weights in an end-to-end
manner. The fused LUT is then used to transform the source image into the
target tone in an efficient way. Extensive results show that our model
outperforms SOTA image enhancement methods on public datasets both subjectively
and objectively, and that our model only takes about 4ms to process a 4K
resolution image on one NVIDIA V100 GPU.
