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

Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables