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.
翻译:图像调色算法旨在自动匹配在不同条件下拍摄的前景图像和背景图像的颜色分布。 以前的深深学习模型忽略了对实际应用至关重要的两个问题, 即高分辨率( HR) 图像处理和模型可理解性。 在本文中, 我们提议为高分辨率图像协调提供一个新的深明易懂彩色过滤器( DCCF) 学习框架。 具体地说, DCCF 将原始输入图像降为低分辨率( LR) 反面部分, 然后以端到端的方式学习四种人类可理解的神经过滤器( 即 hue、 饱和、 价值 和 专注显示过滤器), 最后将这些过滤器应用到原始输入图像中以获得统一的结果。 从可理解神经过滤器中获益, 我们可以为用户提供一个简单而高效的处理器, 与深层模型合作, 在必要时以很少努力获得所期望的结果。 广泛的实验展示 DCCFE 学习框架的有效性, 并且它以端到端的方式超越了对 iHarmon4 数据设置的状态后处理方法, 最后将这些过滤这些过滤器应用到原始输入图像的原始输入结果。 M63%和相对分辨率的M69%。