  This paper presents a new method, called FlexiCurve, for photo enhancement.
Unlike most existing methods that perform image-to-image mapping, which
requires expensive pixel-wise reconstruction, FlexiCurve takes an input image
and estimates global curves to adjust the image. The adjustment curves are
specially designed for performing piecewise mapping, taking nonlinear
adjustment and differentiability into account. To cope with challenging and
diverse illumination properties in real-world images, FlexiCurve is formulated
as a multi-task framework to produce diverse estimations and the associated
confidence maps. These estimations are adaptively fused to improve local
enhancements of different regions. Thanks to the image-to-curve formulation,
for an image with a size of 512*512*3, FlexiCurve only needs a lightweight
network (150K trainable parameters) and it has a fast inference speed (83FPS on
a single NVIDIA 2080Ti GPU). The proposed method improves efficiency without
compromising the enhancement quality and losing details in the original image.
The method is also appealing as it is not limited to paired training data, thus
it can flexibly learn rich enhancement styles from unpaired data. Extensive
experiments demonstrate that our method achieves state-of-the-art performance
on photo enhancement quantitively and qualitatively.
