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Special Issue "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: 15 January 2024 | Viewed by 395

Special Issue Editor

Dr. Chau-Jern Cheng
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

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

Published Papers (1 paper)

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A Physics-Inspired Deep Learning Framework for an Efficient Fourier Ptychographic Microscopy Reconstruction under Low Overlap Conditions
Sensors 2023, 23(15), 6829; - 31 Jul 2023
Viewed by 300
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)
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