# Filter Style Transfer between Photos

arXiv: https://arxiv.org/abs/2007.07925 · HTML: https://mlsanity.com/paper/2007.07925/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes](https://mlsanity.com/paper/2103.17185/) (2021)
- [Neural Preset for Color Style Transfer](https://mlsanity.com/paper/2303.13511/) (2023)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples](https://mlsanity.com/paper/2110.10366/) (2021)
- [INRetouch: Context Aware Implicit Neural Representation for Photography Retouching](https://mlsanity.com/paper/2412.03848/) (2024)
- [CAMS: Color-Aware Multi-Style Transfer](https://mlsanity.com/paper/2106.13920/) (2021)
- [SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing](https://mlsanity.com/paper/2112.00180/) (2021)
- [CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation](https://mlsanity.com/paper/2606.01638/) (2026)
- [Style-Guided Inference of Transformer for High-resolution Image Synthesis](https://mlsanity.com/paper/2210.05533/) (2022)
- [AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters](https://mlsanity.com/paper/2605.12937/) (2026)

## Abstract (arXiv)

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.