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Image-adaptive lookup tables (LUTs) have achieved great success in real-timeimage enhancement tasks due to their high efficiency for modeling colortransforms. However, they embed the complete transform, including the colorcomponent-independent and the component-correlated parts, into only a singletype of LUTs, either 1D or 3D, in a coupled manner. This scheme raises adilemma of improving model expressiveness or efficiency due to two factors. Onthe one hand, the 1D LUTs provide high computational efficiency but lack thecritical capability of color components interaction. On the other, the 3D LUTspresent enhanced component-correlated transform capability but suffer fromheavy memory footprint, high training difficulty, and limited cell utilization.Inspired by the conventional divide-and-conquer practice in the image signalprocessor, we present SepLUT (separable image-adaptive lookup table) to tacklethe above limitations. Specifically, we separate a single color transform intoa cascade of component-independent and component-correlated sub-transformsinstantiated as 1D and 3D LUTs, respectively. In this way, the capabilities oftwo sub-transforms can facilitate each other, where the 3D LUT complements theability to mix up color components, and the 1D LUT redistributes the inputcolors to increase the cell utilization of the 3D LUT and thus enable the useof a more lightweight 3D LUT. Experiments demonstrate that the proposed methodpresents enhanced performance on photo retouching benchmark datasets than thecurrent state-of-the-art and achieves real-time processing on both GPUs andCPUs.

SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement