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A new patchwork simulation method with control of the local-mean histogram

机译:一种控制局部均值直方图的新的拼凑仿真方法

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We present a new stochastic simulation method that builds two-dimensional images by assembling together square image pieces called blocks. The blocks are taken from a reference image. Our method, called patchwork simulation method (PSM), enforces pattern continuity in the image. Moreover, PSM allows to control the image local-mean histogram. This histogram bin-frequencies can be set to user-defined target values that may differ from the reference image local-mean histogram. This flexibility enhances the PSM generality by enlarging the set of all possible simulations. The local-mean histogram control is achieved by adjusting suitably the transition probabilities that associate a new block to an existing neighborhood in the partly simulated image. For several types of synthetic images and one polymer blend image, we show that PSM reproduces faithfully the reference image visual appearance (i.e. patterns are correctly shaped) and that simulated images are statistically compatible with the target local-mean histogram. Moreover, we show that our method has the ability to produce simulations that respect conditional hard data as well as a target local-mean histogram.
机译:我们提出了一种新的随机模拟方法,该方法通过将称为块的正方形图像块组装在一起来构建二维图像。这些块取自参考图像。我们的方法称为拼凑仿真方法(PSM),可在图像中增强图案连续性。此外,PSM允许控制图像局部平均直方图。可以将该直方图bin频率设置为用户定义的目标值,该目标值可能与参考图像局部平均直方图不同。这种灵活性通过扩大所有可能的模拟集来增强PSM的通用性。局部平均直方图控制是通过适当地调整将新块与部分模拟图像中的现有邻域关联的过渡概率来实现的。对于几种类型的合成图像和一个聚合物共混图像,我们表明PSM能够忠实地复制参考图像的视觉外观(即图案形状正确),并且模拟图像在统计上与目标局部平均直方图兼容。此外,我们证明了我们的方法能够产生尊重条件性硬数据以及目标局部均值直方图的模拟。

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