Least MSE Regression for View Synthesis
Least MSE Regression for View Synthesis
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DOI：
10.1109/3dv.2014.29
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发表时间：
2014-12
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影响因子：
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通讯作者：
Keita Takahashi;T. Fujii
中科院分区：
文献类型：
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作者：
Keita Takahashi;T. Fujii
视图合成是将给定的多视图图像组合在一起，从新的视点生成图像的过程。假设新视图的每个像素都是输入视图中相应像素的加权和，我们关注的问题是如何优化每个输入视图的权重。我们的加权方法被称为最小均方误差（MSE）回归，因为它被表述为一个回归问题，其中利用视点之间的二阶统计量来最小化结果图像的MSE。更具体地说，视点之间的亲和关系表示为协方差，并使用线性模型进行近似，该模型的参数适用于每个数据集。利用近似的协方差，可以成功地估计出最优权重。因此，使用我们的方法获得的权重依赖于数据，并且与使用当前经验方法（如距离惩罚）获得的权重有很大不同。如果给定的通信由于噪声而不完全准确，我们的方法仍然有效。我们报告了使用几个多视图数据集的实验结果来验证我们的理论和方法。
View synthesis is the process of combining given multi-view images to generate an image from a new viewpoint. Assuming that each pixel of the new view is obtained as the weighted sum of the corresponding pixels from the input views, we focus on the problem of how to optimize the weight for each of the input views. Our weighting method is called least mean squared error (MSE) regression because it is formulated as a regression problem in which second order statistics among the viewpoints are exploited to minimize the MSE of the resulting image. More specifically, the affinity across the viewpoints is represented as a covariance and approximated using a linear model whose parameters are adapted for each dataset. By using the approximated covariance, the optimal weights can be successfully estimated. As a result, the weights derived using our method are data dependent and significantly differ from those obtained using current empirical methods such as distance penalty. Our method is still effective if the given correspondence is not completely accurate due to noise. We report on experimental results using several multi-view datasets to validate our theory and method.