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首页> 外文期刊>IEEE transactions on multimedia >Non-Local Texture Optimization With Wasserstein Regularization Under Convolutional Neural Network
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Non-Local Texture Optimization With Wasserstein Regularization Under Convolutional Neural Network

机译：卷积神经网络下与Wasserstein正规化的非局部纹理优化

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摘要

Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision, and image processing. Nevertheless, synthesizing structured textures remains a challenging task. Most previous methods rely on additional guidance channels, which encode the structured features of textures. However, estimating the guidance channel is very difficult, and often fails when a texture has unpronounced features. In this paper, we propose a novel texture synthesis method, based on non-local operators, which captures the long-range structure of a texture without the additional guidance channel. The synthesized texture is generated by minimizing non-local texture energy through an expectation-maximization like optimization algorithm. A statistical constraint based on the Wasserstein distance is also proposed to ensure that the synthesized texture preserves the global statistics of the exemplar texture. Extensive experiments show that the proposed method can stably handle textures with different scale structures.
机译：基于示例的纹理合成旨在从示例纹理生成新的纹理，并长期以来一直在计算机图形，计算机视觉和图像处理领域引起注意。然而，合成结构化纹理仍然是一个具有挑战性的任务。最先前的方法依赖于附加的引导通道，其编码纹理的结构化功能。但是，估计引导信道是非常困难的，并且当纹理具有未强调的功能时通常会失败。在本文中，我们提出了一种基于非本地运算符的新颖纹理综合方法，其捕获纹理的远程结构而不进行附加的引导通道。通过期望最大化等优化算法最小化非局部纹理能量来产生合成的纹理。还提出了一种基于Wasserstein距离的统计约束，以确保合成纹理保留示例纹理的全局统计数据。广泛的实验表明，该方法可以稳定地用不同的刻度结构处理纹理。

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