插值(计算机图形学)
算法
双线性插值
图像缩放
估计员
阶梯插值
计算机科学
噪音(视频)
正确性
最近邻插值
反向
数学
图像(数学)
多元插值
图像处理
人工智能
计算机视觉
统计
几何学
作者
Yong Su,Qingchuan Zhang,Xiaoyu Xu,Zeren Gao,Shangquan Wu
标识
DOI:10.1016/j.optlaseng.2017.09.013
摘要
It is believed that the classic forward additive Newton–Raphson (FA-NR) algorithm and the recently introduced inverse compositional Gauss–Newton (IC-GN) algorithm give rise to roughly equal interpolation bias. Questioning the correctness of this statement, this paper presents a thorough analysis of interpolation bias for the IC-GN algorithm. A theoretical model is built to analytically characterize the dependence of interpolation bias upon speckle image, target image interpolation, and reference image gradient estimation. The interpolation biases of the FA-NR algorithm and the IC-GN algorithm can be significantly different, whose relative difference can exceed 80%. For the IC-GN algorithm, the gradient estimator can strongly affect the interpolation bias; the relative difference can reach 178%. Since the mean bias errors are insensitive to image noise, the theoretical model proposed remains valid in the presence of noise. To provide more implementation details, source codes are uploaded as a supplement.
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