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Ignacio Garcia-Dorado, Pascal Getreuer, Bartlomiej Wronski, Peyman Milanfar. “Image Stylization: From Predefined to Personalized.” IET Computer Vision, vol. Computer Vision for the Creative Industries, 2020.

Article permalink: https://doi.org/10.1049/iet-cvi.2019.0787

    title={Image Stylization: From Predefined to Personalized},
    author={Ignacio Garcia-Dorado and Pascal Getreuer and
            Bartlomiej Wronski and Peyman Milanfar},
    journal={IET Computer Vision},
    volume={Computer Vision for the Creative Industries},


We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering techniques are extended and combined to allow the user to unleash their creativity to intuitively invent, modify, and tune new styles from a given set of filters. In parallel to this manual design, we propose a novel procedural approach that automatically assembles sequences of filters, leading to unique and novel styles. An important aim of our framework is to allow for interactive exploration and design, as well as to enable videos and camera streams to be stylized on the fly. In order to achieve this real-time performance, we use the Best Linear Adaptive Enhancement (BLADE) framework – an interpretable shallow machine learning method that simulates complex filter blocks in real time. Our representative results include over a dozen styles designed using our interactive tool, a set of styles created procedurally, and new filters trained with our BLADE approach.

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/).