Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China; Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China; School of Integrated Circuits, Anhui University, Hefei, Anhui Province, 230601, China; Anhui Provincial High-performance Integrated Circuit Engineering Research Center, Anhui University, Hefei, Anhui Province, 230601, China.
Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, Anhui Province, 230601, China.
Comput Biol Med. 2024 Jul;177:108644. doi: 10.1016/j.compbiomed.2024.108644. Epub 2024 May 22.
Traditional multislice iterative phase retrieval (MIPR) from snapshot two-dimensional measurements suffers from the two limitations of pre-defined support and iterative stagnation. To eliminate the requirements for priori knowledge of support masks, this paper proposes a multislice iterative phase retrieval algorithm based on compressed support detection and hybrid input-output algorithm (CSD-MIPR-HIO). The CSD-MIPR-HIO algorithm firstly uses compressed support detection to adaptively detect the support masks of each plane from single 2D diffraction intensity, and then uses a hybrid input-output (HIO) iterative algorithm for MIPR. The proposed method breaks the limitations of traditional MIPR algorithms on priori knowledge of support masks and achieve high-quality reconstruction in noisy environments. Numerical and optical experiments confirm the feasibility, superiority, and robustness of our proposed CSD-MIPR-HIO method.
传统的基于快照二维测量的多层面相恢复（MIPR）算法受到预定义支撑区域和迭代停滞的限制。为了消除对支撑掩模先验知识的需求，本文提出了一种基于压缩支撑检测和混合输入输出算法（CSD-MIPR-HIO）的多层面相恢复算法。CSD-MIPR-HIO 算法首先使用压缩支撑检测从单个 2D 衍射强度中自适应地检测每个平面的支撑掩模，然后使用混合输入输出（HIO）迭代算法进行 MIPR。所提出的方法打破了传统 MIPR 算法对支撑掩模先验知识的限制，并在噪声环境下实现了高质量的重建。数值和光学实验验证了我们提出的 CSD-MIPR-HIO 方法的可行性、优越性和鲁棒性。
