亚像素插值与匹配的经验滤波估计

B. Triggs
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引用次数: 34

摘要

我们研究了亚像素图像平移后像素强度预测的低级问题。它是图像翘曲和超分辨率的基本子程序,对图像相关匹配的精度有重要影响。而不是使用传统的频率空间滤波理论或特设插值器,如样条,我们采取经验的方法,找到最优的亚像素插值滤波器通过直接数值优化在一个大的训练样本集。训练集是通过使用模拟真实像素空间响应函数的子采样器,对不同平移位置的较大图像进行子采样而生成的。我们认为这给出了真实的结果,并在传统和鲁棒预测误差指标下设计了各种不同参数形式的滤波器。我们系统地研究了所得滤波器的性能,特别注意底层图像采样制度的影响和混叠(“锯齿”)的影响。我们总结了这些结果,并给出了获得亚像素精度的实用建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empirical filter estimation for subpixel interpolation and matching
We study the low-level problem of predicting pixel intensities after subpixel image translations. This is a basic subroutine for image warping and super-resolution, and it has a critical influence on the accuracy of subpixel matching by image correlation. Rather than using traditional frequency-space filtering theory or ad hoc interpolators such as splines, we take an empirical approach, finding optimal subpixel interpolation filters by direct numerical optimization over a large set of training examples. The training set is generated by subsampling larger images at different translations, using subsamplers that mimic the spatial response functions of real pixels. We argue that this gives realistic results, and design filters of various different parametric forms under traditional and robust prediction error metrics. We systematically study the performance of the resulting filters, paying particular attention to the influence of the underlying image sampling regime and the effects of aliasing ("jaggies"). We summarize the results and give practical advice for obtaining subpixel accuracy.
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