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.

本文提出了一种用于高分辨率图像和可理解的模型的图像色彩协调算法，通过学习人类可理解的神经滤镜并将其应用于原始输入图像，实现对图像进行协调，与现有后处理方法相比，在iHarmony4数据集上分别实现了MSE和PSNR的7.63％和1.69％相对改善，表明了该方法的有效性。

DCCF：高分辨率图像融合的深度可理解色彩滤镜学习框架