  Digital artists often improve the aesthetic quality of digital photographs
through manual retouching. Beyond global adjustments, professional image
editing programs provide local adjustment tools operating on specific parts of
an image. Options include parametric (graduated, radial filters) and
unconstrained brush tools. These highly expressive tools enable a diverse set
of local image enhancements. However, their use can be time consuming, and
requires artistic capability. State-of-the-art automated image enhancement
approaches typically focus on learning pixel-level or global enhancements. The
former can be noisy and lack interpretability, while the latter can fail to
capture fine-grained adjustments. In this paper, we introduce a novel approach
to automatically enhance images using learned spatially local filters of three
different types (Elliptical Filter, Graduated Filter, Polynomial Filter). We
introduce a deep neural network, dubbed Deep Local Parametric Filters
(DeepLPF), which regresses the parameters of these spatially localized filters
that are then automatically applied to enhance the image. DeepLPF provides a
natural form of model regularization and enables interpretable, intuitive
adjustments that lead to visually pleasing results. We report on multiple
benchmarks and show that DeepLPF produces state-of-the-art performance on two
variants of the MIT-Adobe-5K dataset, often using a fraction of the parameters
required for competing methods.

Computer Vision and Pattern Recognition