基于字典学习的噪声衍射图像相位恢复
Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns
摘要：
提出了一种新的图像相位恢复算法，用于从所寻找的复图像的噪声线性组合（通常是傅里叶变换）的幅度恢复复值图像。该算法是使用交替投影框架开发的，旨在获得高性能的严重噪声（泊松或高斯）的意见。目标图像的估计被重新表述为复域中的稀疏回归，通常称为稀疏编码。这是通过从其表示的数据中学习复域字典来实现的，该复域字典经由对代码具有稀疏约束的矩阵因式分解（即，回归系数）。我们的算法，称为字典学习相位检索（DLPR），共同学习所提到的字典和重建未知的目标图像。DLPR的有效性说明通过复杂的图像，模拟和真实的，在那里它显示出明显的优势，比国家的最先进的竞争对手进行的实验。
英文摘要：
This paper proposes a novel algorithm for image phase retrieval, i.e., for recovering complex-valued images from the amplitudes of noisy linear combinations (often the Fourier transform) of the sought complex images. The algorithm is developed using the alternating projection framework and is aimed to obtain high performance for heavily noisy (Poissonian or Gaussian) observations. The estimation of the target images is reformulated as a sparse regression, often termed sparse coding, in the complex domain. This is accomplished by learning a complex domain dictionary from the data it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients). Our algorithm, termed dictionary learning phase retrieval (DLPR), jointly learns the referred to dictionary and reconstructs the unknown target image. The effectiveness of DLPR is illustrated through experiments conducted on complex images, simulated and real, where it shows noticeable advantages over the state-of-the-art competitors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
