  We present a novel approach to adjust global image properties such as colour,
saturation, and luminance using human-interpretable image enhancement curves,
inspired by the Photoshop curves tool. Our method, dubbed neural CURve Layers
(CURL), is designed as a multi-colour space neural retouching block trained
jointly in three different colour spaces (HSV, CIELab, RGB) guided by a novel
multi-colour space loss. The curves are fully differentiable and are trained
end-to-end for different computer vision problems including photo enhancement
(RGB-to-RGB) and as part of the image signal processing pipeline for image
formation (RAW-to-RGB). To demonstrate the effectiveness of CURL we combine
this global image transformation block with a pixel-level (local) image
multi-scale encoder-decoder backbone network. In an extensive experimental
evaluation we show that CURL produces state-of-the-art image quality versus
recently proposed deep learning approaches in both objective and perceptual
metrics, setting new state-of-the-art performance on multiple public datasets.
Our code is publicly available at: https://github.com/sjmoran/CURL.
