Image Stylization: From Predefined to Personalized
This work addresses the need for intuitive and efficient tools for artists and designers to create personalized image styles, though it is incremental in building upon existing filtering techniques.
The paper tackles the problem of interactive image stylization by introducing a framework that combines predefined filter blocks with a novel procedural approach for automatic style generation, achieving real-time performance using the BLADE method.
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 \textit{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.