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基于边缘保持滤波的编辑传播

Edit Propagation via Edge-Aware Filtering

  • 摘要: 本文提出了一种应用于高分辨率图像视频的编辑传播新方法。新方法允许用户在图像或视频上给出粗略的笔画和相应的编辑,然后将这些初始编辑实时地传播到整个图像视频数据集当中。根据实验观察和理论分析,编辑扩散计算可通过运用最新的边缘保持滤波的方式来实现。新方法舍弃了以往求解大规模线性方程组实现全局优化计算的思路,借助边缘保持滤波来处理编辑扩散。与其他编辑扩散方法相比,新方法具有较低的计算复杂性,并非常适合使用GPU进行并行运算加速。实验结果表明了新方法具有很好的易用性并且能够实时处理高分辨率图像和视频。

     

    Abstract: This paper presents a novel framework for efficiently propagating the stroke-based user edits to the regions with similar colors and locations in high resolution images and videos. Our framework is based on the key observation that the edit propagation intrinsically can also be achieved by utilizing recently proposed edge-preserving filters. Therefore, instead of adopting the traditional global optimization which may involve a time-consuming solution, our algorithm propagates edits with the aid of the edge-preserve filters. Such a propagation scheme has low computational complexity and supports multiple kinds of strokes for more flexible user interactions. Further, our method can be easily and efficiently implemented in GPU. The experimental results demonstrate the efficiency and user-friendliness of our approach.

     

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