Social media images are generally transformed by filtering to obtain
aesthetically more pleasing appearances. However, CNNs generally fail to
interpret both the image and its filtered version as the same in the visual
analysis of social media images. We introduce Instagram Filter Removal Network
(IFRNet) to mitigate the effects of image filters for social media analysis
applications. To achieve this, we assume any filter applied to an image
substantially injects a piece of additional style information to it, and we
consider this problem as a reverse style transfer problem. The visual effects
of filtering can be directly removed by adaptively normalizing external style
information in each level of the encoder. Experiments demonstrate that IFRNet
outperforms all compared methods in quantitative and qualitative comparisons,
and has the ability to remove the visual effects to a great extent.
Additionally, we present the filter classification performance of our proposed
model, and analyze the dominant color estimation on the images unfiltered by
all compared methods.

本文介绍了一种名为 IFRNet 的神经网络模型，主要用于社交媒体图片分析应用中的图像滤镜去除。模型实现过程中，将图像滤镜的影响视为样式变换问题，采用自适应归一化外部样式信息的方法，能够在不同编码器层次上直接去除过滤的视觉效果。实验结果展示了 IFRNet 在定量和定性比较中优于所有对比方法，具有很强的滤镜去除能力。此外，本文还对比了模型的滤镜分类性能和未经过滤的图像主导颜色进行了分析。