基于组合系数矩阵的联合图像配准与超分辨率

H. Rezayi, S. Seyedin
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引用次数: 5

摘要

本文提出了一种结合扭曲、模糊和降采样三种主要操作的图像配准和超分辨率联合配准方法。与以前的方法不同,我们既不通过数值计算雅可比矩阵,也不通过分别处理三个主要操作来推导雅可比矩阵。本文提出了一种由三种主运算组合解析导出雅可比矩阵的新方法。实验结果表明,该方法比Tian的IR和sr联合方法具有更好的峰值信噪比(PSNR),并降低了计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint image registration and super-resolution based on combinational coefficient matrix
In this paper we propose a new joint image registration (IR) and super-resolution (SR) method by combining the three principal operations of warping, blurring and down-sampling. Unlike previous methods, we neither calculate the Jacobian matrix numerically nor derive the Jacobian matrix by treating the three principal operations separately. We develop a new approach to derive the Jacobian matrix analytically from the combination of the three principal operations. Experimental results show that our method has better Peak Signal-to-Noise Ratio (PSNR) than the recently proposed Tian's joint method of IR and SR. Computational complexity also has been decreased in our proposed method.
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