Resolution Analysis in a Lens-Free On-Chip Digital Holographic Microscope

Abstract

Lens-free on-chip digital holographic microscopy (LFOCDHM) is a modern imaging technique whereby the sample is placed directly onto or very close to the digital sensor, and illuminated by a partially coherent source located far above it. The scattered object wave interferes with the reference (unscattered) wave at the plane where a digital sensor is situated, producing a digital hologram that can be processed in several ways to extract and numerically reconstruct an in-focus image using the back propagation algorithm. Without requiring any lenses and other intermediate optical components, the LFOCDHM has unique advantages of offering a large effective numerical aperture (NA) close to unity across the native wide field-of-view (FOV) of the imaging sensor in a cost-effective and compact design. However, unlike conventional coherent diffraction limited imaging systems, where the limiting aperture is used to define the system performance, typical lens-free microscopes only produce compromised imaging resolution that far below the ideal coherent diffraction limit. At least five major factors may contribute to this limitation, namely, the sample-to-sensor distance, spatial and temporal coherence of the illumination, finite size of the equally spaced sensor pixels, and finite extent of the image sub-FOV used for the reconstruction, which have not been systematically and rigorously explored until now. In this work, we derive five transfer function models that account for all these physical effects and interactions of these models on the imaging resolution of LFOCDHM. We also examine how our theoretical models can be utilized to optimize the optical design or predict the theoretical resolution limit of a given LFOCDHM system. We present a series of simulations and experiments to confirm the validity of our theoretical models.

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

“…Several kinds of natural hazards related environment problems has been solved by various research groups for sustainable management and utilization of natural resources (Pradhan and Kim, 2017;Jiang et al, 2018;Tsai et al, 2019;Wang et al, 2020;Xu et al, 2021) with the help of remote sensing (RS) technology (Han et al, 2019;Hu et al, 2020;Zhang et al, 2020c) and geographical information system (GIS) tool (Zuo et al, 2015;Xu et al, 2018;Zhu et al, 2019;Yang et al, 2020b) and widespread progress in the computational facilities (Chao et al, 2018;Zhang et al, 2018Zhang et al, , 2020bCao et al, 2020;Xu et al, 2020;Feng et al, 2020). A noteworthy support from the combination of RS and GIS technology gives efficiently solution in the several types of natural hazards related problems (Yang et al, 2018(Yang et al, , 2020aSun et al, 2021).…”

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

“…Consequently, the necessity of designing an accurate (Liu et al 2021 ; Yang and Sowmya 2015 ; Zhang et al 2020b , c ) and real-time detector (Ran et al 2020 ; Wang 2020 ; Zuo et al 2015 ) has become more prominent. Besides, this review on COVID19 detection systems shows that most of the existing deep learning-based systems have used deep CNN-based networks (Li et al 2019a ; Ma et al 2019 ; Xu et al 2020 ; Yang et al 2020a , 2021 ); thereby, we propose to employ the ability of deep CNN as a COVID 19 detector.…”

Autofocusing Algorithm for Pixel-Super-Resolved Lensfree On-Chip Microscopy

“…Nevertheless, it is difficult for traditional microscopic imaging technology to satisfy the large field-of-view and high-resolution at the same time. The recently developed lensfree on-chip microscopy provides a new opportunity to process high-resolution and large field-of-view images, which has the advantages of small size, low cost, and excellent performance [6][7][8][9][10][11][12][13][14]. Lensfree on-chip microscopy reconstructed the image of a specimen based on the interference patterns created by the diffracted object.…”

“…In order to solve this problem, superresolution algorithms that process a series of low-resolution holograms have been applied to lensfree imaging [15][16][17][18]. Many related studies have been proposed and can be classified as: twodimensional movement of light sources by mechanical structures [19], tightly distributed fiber arrays [14], multi-height superresolution algorithm [11], different illumination wavelengths [18], different illumination angles [20], and so on.…”