  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.
