# WISE: Whitebox Image Stylization by Example-based Learning

arXiv: https://arxiv.org/abs/2207.14606 · HTML: https://mlsanity.com/paper/2207.14606/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 71 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 30 of 31)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [Controlling Perceptual Factors in Neural Style Transfer](https://mlsanity.com/paper/1611.07865/) (2016)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Paint Transformer: Feed Forward Neural Painting with Stroke Prediction](https://mlsanity.com/paper/2108.03798/) (2021)
- [A Style-Aware Content Loss for Real-time HD Style Transfer](https://mlsanity.com/paper/1807.10201/) (2018)
- [Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes](https://mlsanity.com/paper/2103.17185/) (2021)
- [Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields](https://mlsanity.com/paper/1802.07101/) (2018)
- [Neural Painters: A learned differentiable constraint for generating brushstroke paintings](https://mlsanity.com/paper/1904.08410/) (2019)
- [Exposure: A White-Box Photo Post-Processing Framework](https://mlsanity.com/paper/1709.09602/) (2017)
- [Natural Image Noise Dataset](https://mlsanity.com/paper/1906.00270/) (2019)
- [Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer](https://mlsanity.com/paper/1904.02296/) (2019)
- [BLADE: Filter Learning for General Purpose Computational Photography](https://mlsanity.com/paper/1711.10700/) (2017)
- [MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing](https://mlsanity.com/paper/2004.10634/) (2020)

## Built on by (most cited first, 5 of 5)

- [AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks](https://mlsanity.com/paper/2403.14468/) (2024)
- [StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer](https://mlsanity.com/paper/2405.05027/) (2024)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [Controlling Geometric Abstraction and Texture for Artistic Images](https://mlsanity.com/paper/2308.00148/) (2023)
- [Stylistic Attribute Control in Latent Diffusion Models](https://mlsanity.com/paper/2605.02583/) (2026)

## Abstract (arXiv)

Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering. In contrast to deep learning-based methods, these heuristics-based filtering techniques can operate on high-resolution images, are interpretable, and can be parameterized according to various design aspects. However, adapting or extending these techniques to produce new styles is often a tedious and error-prone task that requires expert knowledge. We propose a new paradigm to alleviate this problem: implementing algorithmic image filtering techniques as differentiable operations that can learn parametrizations aligned to certain reference styles. To this end, we present WISE, an example-based image-processing system that can handle a multitude of stylization techniques, such as watercolor, oil or cartoon stylization, within a common framework. By training parameter prediction networks for global and local filter parameterizations, we can simultaneously adapt effects to reference styles and image content, e.g., to enhance facial features. Our method can be optimized in a style-transfer framework or learned in a generative-adversarial setting for image-to-image translation. We demonstrate that jointly training an XDoG filter and a CNN for postprocessing can achieve comparable results to a state-of-the-art GAN-based method.