  Recent years have witnessed the increasing popularity of learning based
methods to enhance the color and tone of photos. However, many existing photo
enhancement methods either deliver unsatisfactory results or consume too much
computational and memory resources, hindering their application to
high-resolution images (usually with more than 12 megapixels) in practice. In
this paper, we learn image-adaptive 3-dimensional lookup tables (3D LUTs) to
achieve fast and robust photo enhancement. 3D LUTs are widely used for
manipulating color and tone of photos, but they are usually manually tuned and
fixed in camera imaging pipeline or photo editing tools. We, for the first time
to our best knowledge, propose to learn 3D LUTs from annotated data using
pairwise or unpaired learning. More importantly, our learned 3D LUT is
image-adaptive for flexible photo enhancement. We learn multiple basis 3D LUTs
and a small convolutional neural network (CNN) simultaneously in an end-to-end
manner. The small CNN works on the down-sampled version of the input image to
predict content-dependent weights to fuse the multiple basis 3D LUTs into an
image-adaptive one, which is employed to transform the color and tone of source
images efficiently. Our model contains less than 600K parameters and takes less
than 2 ms to process an image of 4K resolution using one Titan RTX GPU. While
being highly efficient, our model also outperforms the state-of-the-art photo
enhancement methods by a large margin in terms of PSNR, SSIM and a color
difference metric on two publically available benchmark datasets.
