论文标题

图像样式:从预定义到个性化

Image Stylization: From Predefined to Personalized

论文作者

Garcia-Dorado, Ignacio, Getreuer, Pascal, Wronski, Bartlomiej, Milanfar, Peyman

论文摘要

我们提出了一个使用各种预定义过滤器块的新图像样式进行交互式设计的框架。新颖和现成的图像过滤和渲染技术都被扩展并组合在一起,以使用户可以从给定的过滤器中释放其创造力,从而从直觉上发明,修改和调整新样式。与这种手动设计并行,我们提出了一种新型的程序方法,该方法自动组装了过滤器序列,从而导致独特和新颖的样式。我们框架的一个重要目的是允许交互式探索和设计,以及使视频和相机流的风格化。为了实现这一实时性能,我们使用\ textIt {最佳线性自适应增强}(刀片)框架 - 一种可解释的浅机学习方法,可实时模拟复杂的滤波器块。我们的代表性结果包括使用我们的交互式工具设计的十几种样式,一组程序制作的样式以及通过刀片方法训练的新滤镜。

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.

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