Pattern Recognition ( IF 8 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.patcog.2021.107990 Joo Dong Yun , Yunho Kim
We propose a random Fourier sampling scheme to enhance the accuracy of the high frequency pattern estimation for image reconstruction. This method is designed to work in a constrained minimization based on the Fourier-Haar interplay revealing a column-wise maximum coherent structure that we provide. Essential in the scheme is to generate a data-driven density function by a small percentage of Fourier samples. The density function governs a random sampling procedure to acquire high frequency information, resulting in better reconstruction of the Haar wavelet coefficients. We also discuss a few examples of exact recovery of the Haar wavelet coefficients from which the proposed sampling scheme has emerged. Numerical experiments confirm superiority of the proposed sampling scheme to other conventional sampling schemes in the framework.
中文翻译:
图像重建的两阶段自适应随机傅里叶采样方法
我们提出了一种随机傅里叶采样方案,以提高用于图像重建的高频模式估计的准确性。此方法旨在在受约束的环境中工作基于傅里叶-哈尔相互作用的最小化揭示了我们提供的列级最大相干结构。该方案必不可少的是通过一小部分的傅立叶样本生成数据驱动的密度函数。密度函数控制随机采样过程以获取高频信息,从而更好地重建Haar小波系数。我们还讨论了Haar小波系数精确恢复的一些示例,这些示例正是从这些示例中出现的。数值实验证实了所提出的抽样方案优于其他传统抽样方案。 框架。