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Image enhancement aims at improving the aesthetic visual quality of photos byretouching the color and tone, and is an essential technology for professionaldigital photography. Recent years deep learning-based image enhancementalgorithms have achieved promising performance and attracted increasingpopularity. However, typical efforts attempt to construct a uniform enhancerfor all pixels' color transformation. It ignores the pixel differences betweendifferent content (e.g., sky, ocean, etc.) that are significant forphotographs, causing unsatisfactory results. In this paper, we propose a novellearnable context-aware 4-dimensional lookup table (4D LUT), which achievescontent-dependent enhancement of different contents in each image viaadaptively learning of photo context. In particular, we first introduce alightweight context encoder and a parameter encoder to learn a context map forthe pixel-level category and a group of image-adaptive coefficients,respectively. Then, the context-aware 4D LUT is generated by integratingmultiple basis 4D LUTs via the coefficients. Finally, the enhanced image can beobtained by feeding the source image and context map into fused context-aware4D~LUT via quadrilinear interpolation. Compared with traditional 3D LUT, i.e.,RGB mapping to RGB, which is usually used in camera imaging pipeline systems ortools, 4D LUT, i.e., RGBC(RGB+Context) mapping to RGB, enables finer control ofcolor transformations for pixels with different content in each image, eventhough they have the same RGB values. Experimental results demonstrate that ourmethod outperforms other state-of-the-art methods in widely-used benchmarks.

4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement