论文标题
部分可观测时空混沌系统的无模型预测
Generalized Rectifier Wavelet Covariance Models For Texture Synthesis
论文作者
论文摘要
最新的纹理合成最大熵模型是由依赖于卷积神经网络（CNN）定义的图像表示的统计数据构建的。在这方面，此类表示捕获了丰富的结构，在这方面优于基于小波的表示。但是，与神经网络相反，小波提供了有意义的表示，因为它们众所周知，它们可以在图像中的多个尺度（例如边缘）检测结构。在这项工作中，我们提出了一个基于非线性小波表示的统计家庭，可以使用广义的整流器非线性将其视为单层CNN的特定实例。这些统计数据可显着提高以前经典小波的模型的视觉质量，并允许在灰度和颜色纹理上生成与最先进模型相似的合成。
State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN). Such representations capture rich structures in texture images, outperforming wavelet-based representations in this regard. However, conversely to neural networks, wavelets offer meaningful representations, as they are known to detect structures at multiple scales (e.g. edges) in images. In this work, we propose a family of statistics built upon non-linear wavelet based representations, that can be viewed as a particular instance of a one-layer CNN, using a generalized rectifier non-linearity. These statistics significantly improve the visual quality of previous classical wavelet-based models, and allow one to produce syntheses of similar quality to state-of-the-art models, on both gray-scale and color textures.
