Sparsity assisted phase retrieval of complex valued objects

Charu Gaur, K. Khare
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引用次数: 3

Abstract

Iterative phase retrieval of complex valued objects (phase objects) suffers from twin image problem due to the presence of features of image and its complex conjugate in the recovered solution. The twin-image problem becomes more severe when object support is centro-symmetric. In this paper, we demonstrate that by modifying standard Hybrid-Input output (HIO) algorithm using an adaptive sparsity enhancement step, the twin-image problem can be addressed successfully even when the object support is centro-symmetric. Adaptive sparsity enhanced algorithm and numerical simulation for binary as well as gray scale phase objects are presented. The high quality phase recovery results presented here show the effectiveness of adaptive sparsity enhanced algorithm.
稀疏性辅助复值对象相位检索
复值对象(相位对象)的迭代相位检索由于图像的特征及其复共轭存在于恢复解中而面临双象问题。当对象支持为中心对称时,双像问题变得更加严重。在本文中,我们证明了通过使用自适应稀疏增强步骤修改标准的混合输入输出(HIO)算法,即使对象支持是中心对称的,也可以成功地解决双图像问题。提出了二值和灰度相目标的自适应稀疏增强算法和数值模拟。高质量的相位恢复结果表明了自适应稀疏度增强算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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