The phase retrieval (PR), reconstructing an object from its Fourier magnitudes, is equivalent to a nonlinear inverse problem. In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deals with the fine feature set accordingly. Since the KSVD allows the content of oversampled dictionary with sparse representation to adaptively fit a given set of object examples, as long as the ER/HIO algorithms provide decent object estimation at early stage, the pixels violating the object constraint can be restored with superior image quality. The numerical analyses demonstrated the effectiveness of ER + KSVD and HIO + KSVD through multiple independent initial Fourier phases. With its versatility and simplicity, the proposed method can be generalized to be implemented with more PR state-of-the-arts.

中文翻译：

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包含基于稀疏字典学习的修复方法的改进的用于相位检索的减少错误和混合输入输出算法

从傅立叶量级重建对象的相位检索（PR）等效于非线性逆问题。在本文中，我们提出了一种两步算法，传统的ER / HIO迭代充当粗糙特征的重构，而基于KSVD的修复技术则相应地处理了精细特征集。由于KSVD允许具有稀疏表示形式的过采样字典的内容自适应地适应给定的一组对象示例，因此，只要ER / HIO算法在早期就提供了不错的对象估计，则可以用优质图像恢复违反对象约束的像素质量。数值分析证明了ER + KSVD和HIO + KSVD通过多个独立的初始傅立叶阶段的有效性。凭借其多功能性和简单性，