Fast automatic multiple positioning for lensless coherent diffraction imaging. (August 2022)

• Record Type: Journal Article
• Authors: Zhou, Xuyang; Wen, Xiu; Ji, Yu; Geng, Yong; Liu, Shutian; Liu, Zhengjun
• Is Part Of: Optics and lasers in engineering. Volume 155(2022)
• Publication Date: 2022-08
• Subjects: Fast auto-positioning algorithm -- Phase retrieval -- Multi-distance -- Image reconstruction; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.107055
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: This work simplifies the CDI system by using a one-dimension guide instead of high-precision translation stages making the recording of diffractogram easier and more flexible. A multi-position automatic positioning algorithm combining subdivision search and image clarity evaluation function is proposed to obtain diffraction distances fast and accurately. A strategy of recording diffractogram with increasing intervals is proposed to improve the quality of image reconstruction. This technology improves the efficiency of fast automatic positioning of multidistance CDI, making the preparation of image reconstruction more convenient and efficient. Abstract: The distance errors in coherent diffraction imaging degrade reconstructed images. In this paper, a fast auto-positioning algorithm based on subdivision search is proposed to calculate the position of each image. The search scheme can obtain diffraction distances quickly and accurately. For the same range and search accuracy, the calculation speed of one pattern in this method is approximately 30 times that of the traversal search algorithm. The proposed strategy recording patterns with increasing intervals improves the convergence of iterative phase retrieval. The recovered images are high quality since the influence of ripple waves is limited. This method is beneficial to the robustness and flexibility of multi-distance computational imaging.