  Over the past few years, image-to-image style transfer has risen to the
frontiers of neural image processing. While conventional methods were
successful in various tasks such as color and texture transfer between images,
none could effectively work with the custom filter effects that are applied by
users through various platforms like Instagram. In this paper, we introduce a
new concept of style transfer, Filter Style Transfer (FST). Unlike conventional
style transfer, new technique FST can extract and transfer custom filter style
from a filtered style image to a content image. FST first infers the original
image from a filtered reference via image-to-image translation. Then it
estimates filter parameters from the difference between them. To resolve the
ill-posed nature of reconstructing the original image from the reference, we
represent each pixel color of an image to class mean and deviation. Besides, to
handle the intra-class color variation, we propose an uncertainty based
weighted least square method for restoring an original image. To the best of
our knowledge, FST is the first style transfer method that can transfer custom
filter effects between FHD image under 2ms on a mobile device without any
textual context loss.
