  Image color harmonization algorithm aims to automatically match the color
distribution of foreground and background images captured in different
conditions. Previous deep learning based models neglect two issues that are
critical for practical applications, namely high resolution (HR) image
processing and model comprehensibility. In this paper, we propose a novel Deep
Comprehensible Color Filter (DCCF) learning framework for high-resolution image
harmonization. Specifically, DCCF first downsamples the original input image to
its low-resolution (LR) counter-part, then learns four human comprehensible
neural filters (i.e. hue, saturation, value and attentive rendering filters) in
an end-to-end manner, finally applies these filters to the original input image
to get the harmonized result. Benefiting from the comprehensible neural
filters, we could provide a simple yet efficient handler for users to cooperate
with deep model to get the desired results with very little effort when
necessary. Extensive experiments demonstrate the effectiveness of DCCF learning
framework and it outperforms state-of-the-art post-processing method on
iHarmony4 dataset on images' full-resolutions by achieving 7.63% and 1.69%
relative improvements on MSE and PSNR respectively.
