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紧致支撑函数中小尺度部分成像的分辨率增强

Resolution enhancement of imaging small-scale portions in a compactly supported function.

作者信息

Shieh Hsin M, Hsu Yu-Ching, Byrne Charles L, Fiddy Michael A

机构信息

Department of Electrical Engineering, Feng Chia University, 100 Wenhwa Rd., Seatwen, Taichung, Taiwan 40724, China.

出版信息

J Opt Soc Am A Opt Image Sci Vis. 2010 Feb 1;27(2):141-50. doi: 10.1364/JOSAA.27.000141.

Abstract

The ambiguity involved in reconstructing an image from limited Fourier data is removed using a new technique that incorporates prior knowledge of the location of regions containing small-scale features of interest. The prior discrete Fourier transform (PDFT) method for image reconstruction incorporates prior knowledge of the support, and perhaps general shape, of the object function being reconstructed through the use of a weight function. The new approach extends the PDFT by allowing different weight functions to modulate the different spatial frequency components of the reconstructed image. The effectiveness of the new method is tested on one- and two-dimensional simulations.

摘要

使用一种新技术消除了从有限傅里叶数据重建图像时涉及的模糊性,该技术结合了对包含感兴趣的小尺度特征区域位置的先验知识。用于图像重建的先验离散傅里叶变换(PDFT)方法通过使用权重函数,纳入了关于被重建目标函数的支撑以及可能的一般形状的先验知识。新方法通过允许不同的权重函数调制重建图像的不同空间频率分量来扩展PDFT。在一维和二维模拟中测试了新方法的有效性。

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