sensors-logo

Journal Browser

Journal Browser

Digital Holography in Optics: Techniques and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1545

Special Issue Editor


E-Mail Website
Guest Editor
Institute of Electro-Optical Engineering, National Taiwan Normal University, Taipei 11114, Taiwan
Interests: digital holographic; tomography; deep learning; optical tweezers; microscopic; imaging; sensing

Special Issue Information

Dear Colleagues,

Many challenging measurement tasks in production simultaneously have high requirements for accuracy, measurement field size, lateral sampling, and measurement time. Standard machine vision methods are usually based on powerful non-contact measurement and optical inspection approaches. However, the potential applications of these approaches, especially at the micro-/nano-scale, are restricted by the finite depth of field and fixed working distance of imaging devices. Digital holography is an emerging imaging technique incorporating numerical wavefront reconstruction, a technique which uses a digital sensor array (typically a CCD/CMOS image sensor or a similar device) for the acquisition and processing of holograms and records the optical wave diffracted by the object onto the image sensor. The object is reconstructed numerically by propagating the recorded wavefront backward. The object distance becomes a computation parameter that can be chosen arbitrarily and adjusted to match the object position. No refractive lens is used and the usual depth-of-field and working distance limitations are replaced by less restrictive boundaries tied to the laser source coherence length and to the pixel pitch and chip size of the image sensor. Digital holography extends the field of application of machine vision and optical metrology by allowing for the attainment of a large range of depths of focus, working distances, and spatial resolutions that are inaccessible to refractive imaging systems. In this way, it has become a powerful method for optical sensing and metrology applications as it can measure a relatively large field of view with interferometric precision and short acquisition times and assess both reflective and transmissive surfaces simultaneously. Furthermore, the rapid advancement of optical sensing, display, and computing technologies holds great promise for the future development and application of digital holography in optics.

Dr. Chau-Jern Cheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital holography
  • optical metrology
  • deep leaning
  • biological cell imaging
  • optical inspection
  • unconventional imaging
  • resolution enhancement

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 1217 KiB  
Article
Improving the Signal-to-Noise Ratio of Axial Displacement Measurements of Microspheres Based on Compound Digital Holography Microscopy Combined with the Reconstruction Centering Method
by Yanan Zeng, Qihang Guo, Xiaodong Hu, Junsheng Lu, Xiaopan Fan, Haiyun Wu, Xiao Xu, Jun Xie and Rui Ma
Sensors 2024, 24(9), 2723; https://doi.org/10.3390/s24092723 - 24 Apr 2024
Viewed by 290
Abstract
In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise [...] Read more.
In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise increases with measurement accuracy and the signal-to-noise ratio (SNR). In compound digital holography microscopy (CDHM)-based measurements, precise identification of the tracking marker is critical to ensuring measurement precision. The reconstruction centering method (RCM) was proposed to suppress the drawbacks caused by installation errors and, at the same time, improve the correct identification of the tracking marker. The reconstructed center is considered to be the center of the microsphere, rather than the center of imaging in conventional digital holographic microscopy. This method was verified by simulation of rays tracing through microspheres and axial moving experiments. The axial displacements of silica microspheres with diameters of 5 μm and 10 μm were tested by CDHM in combination with the RCM. As a result, the SNR of the proposed method was improved by around 30%. In addition, the method was successfully applied to axial displacement measurements of overlapped microspheres with a resolution of 2 nm. Full article
(This article belongs to the Special Issue Digital Holography in Optics: Techniques and Applications)
13 pages, 11641 KiB  
Article
A Physics-Inspired Deep Learning Framework for an Efficient Fourier Ptychographic Microscopy Reconstruction under Low Overlap Conditions
by Lyes Bouchama, Bernadette Dorizzi, Jacques Klossa and Yaneck Gottesman
Sensors 2023, 23(15), 6829; https://doi.org/10.3390/s23156829 - 31 Jul 2023
Cited by 3 | Viewed by 957
Abstract
Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular [...] Read more.
Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular interest because of its important super-resolution factor. In complement to traditional intensity images, phase images are also produced. A large set of N raw images (with typically N = 225) is, however, required because of the reconstruction process that is involved. In this paper, we address the problem of FPM image reconstruction using a few raw images only (here, N = 37) as is highly desirable to increase microscope throughput. In contrast to previous approaches, we develop an algorithmic approach based on a physics-informed optimization deep neural network and statistical reconstruction learning. We demonstrate its efficiency with the help of simulations. The forward microscope image formation model is explicitly introduced in the deep neural network model to optimize its weights starting from an initialization that is based on statistical learning. The simulation results that are presented demonstrate the conceptual benefits of the approach. We show that high-quality images are effectively reconstructed without any appreciable resolution degradation. The learning step is also shown to be mandatory. Full article
(This article belongs to the Special Issue Digital Holography in Optics: Techniques and Applications)
Show Figures

Figure 1

Back to TopTop