Image stylisation: From predefined to personalised

Abstract

The authors present a framework for interactive design of new image stylisations 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, they propose a novel procedural approach that automatically assembles sequences of filters, leading to unique and novel styles. An important aim of the authors' framework is to allow for interactive exploration and design, as well as to enable videos and camera streams to be stylised on the fly. In order to achieve this real-time performance, they use the Best Linear Adaptive Enhancement (BLADE) framework - an interpretable shallow machine learning method that simulates complex filter blocks in real time. Their representative results include over a dozen styles designed using their interactive tool, a set of styles created procedurally, and new filters trained with their BLADE approach.