# Instagram Filter Removal on Fashionable Images

arXiv: https://arxiv.org/abs/2104.05072 · HTML: https://mlsanity.com/paper/2104.05072/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Real-Time User-Guided Image Colorization with Learned Deep Priors](https://mlsanity.com/paper/1705.02999/) (2017)
- [Exploring the structure of a real-time, arbitrary neural artistic stylization network](https://mlsanity.com/paper/1705.06830/) (2017)
- [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://mlsanity.com/paper/2004.12276/) (2020)
- [Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?](https://mlsanity.com/paper/1811.06604/) (2018)
- [Recognizing Instagram Filtered Images with Feature De-stylization](https://mlsanity.com/paper/1912.13000/) (2019)
- [Manipulating Attributes of Natural Scenes via Hallucination](https://mlsanity.com/paper/1808.07413/) (2018)

## Built on by (most cited first, 8 of 8)

- [Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report](https://mlsanity.com/paper/2210.11153/) (2022)
- [AIM 2022 Challenge on Instagram Filter Removal: Methods and Results](https://mlsanity.com/paper/2210.08997/) (2022)
- [Modeling the Lighting in Scenes as Style for Auto White-Balance Correction](https://mlsanity.com/paper/2210.09090/) (2022)
- [Patch-wise Contrastive Style Learning for Instagram Filter Removal](https://mlsanity.com/paper/2204.07486/) (2022)
- [Reversing Image Signal Processors by Reverse Style Transferring](https://mlsanity.com/paper/2210.09074/) (2022)
- [CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal](https://mlsanity.com/paper/2208.14039/) (2022)
- [Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset](https://mlsanity.com/paper/2604.16114/) (2026)
- [Crafting Query-Aware Selective Attention for Single Image Super-Resolution](https://mlsanity.com/paper/2504.06634/) (2025)

## Abstract (arXiv)

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.