  Automatic color enhancement is aimed to adaptively adjust photos to expected
styles and tones. For current learned methods in this field, global harmonious
perception and local details are hard to be well-considered in a single model
simultaneously. To address this problem, we propose a coarse-to-fine framework
with non-local attention for color enhancement in this paper. Within our
framework, we propose to divide enhancement process into channel-wise
enhancement and pixel-wise refinement performed by two cascaded Convolutional
Neural Networks (CNNs). In channel-wise enhancement, our model predicts a
global linear mapping for RGB channels of input images to perform global style
adjustment. In pixel-wise refinement, we learn a refining mapping using
residual learning for local adjustment. Further, we adopt a non-local attention
block to capture the long-range dependencies from global information for
subsequent fine-grained local refinement. We evaluate our proposed framework on
the commonly using benchmark and conduct sufficient experiments to demonstrate
each technical component within it.
