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Lensless Imaging and Computational Sensing
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Lensless Imaging and Computational Sensing

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 16018

Special Issue Editors

Prof. Dr. Aydogan Ozcan

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Guest Editor
Electrical and Computer Engineering and Bioengineering, Bioengineering Department, California NanoSystems Institute (CNSI), Department of Surgery, University of California, Los Angeles, CA, USA
Interests: computational optical imaging and sensing; mobile health; telemedicine; global health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yair Rivenson

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Guest Editor
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
Interests: computational imaging and sensing for biomedical applications; life sciences and environmental applications; physics-inspired machine learning
Dr. Yichen Wu

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of California, Los Angeles, 420 Westwood Plaza, 14-128B Engr. IV, Los Angeles, CA 90095, USA
Interests: computational imaging; computer vision; biomedical imaging/sensing and machine learning
Dr. Zachary Ballard

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, USA
Interests: computational sensing and machine learning; point-of-care sensing; photonic sensors

Special Issue Information

Dear Colleagues,

Recent advancements in the semiconductor industry have inspired a revolution in compact sensors and computational platforms, creating a paradigm shift from lens-based imaging and sensing to computational imaging and sensing. In extreme cases, computational imaging systems forgo traditional optical hardware including imaging lenses, instead relying on computation to retrieve the relevant imaging information. By eliminating bulky, compound optics, such systems can become compact, cost-effective, and portable, expanding access to imaging and sensing technologies, especially for low-resource settings. Additionally, these computational imaging and sensing systems can take advantage of recent advances in machine learning to infer meaningful information from their nontraditional acquisition hardware. Moreover, the availability of such machine learning tools has led to the creation of data-driven co-design of sensing hardware and software, ushering a new era in sensors designs that present application-specific performance advantages. In this Special Issue of Sensors, we wish to provide a collection of important advances regarding applications of biomedical, chemical, and environmental computational imaging and sensing with focus on (but not limited to) novel design of lens-less imaging or sensing hardware, image processing, and machine learning algorithms for lens-free imaging and sensing, data-driven sensor design, and applications in biomedicine, spectroscopy, and environmental monitoring.

Prof. Dr. Aydogan Ozcan
Prof. Dr. Yair Rivenson
Dr. Yichen Wu
Dr. Zachary Ballard
Guest Editors

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

  • Lens-free imaging
  • On-chip holographic microscopy
  • Computational imaging and sensing
  • Cross-modality sensing
  • Data-driven sensor design
  • Deep learning in imaging and sensing applications

Published Papers (5 papers)

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Research

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17 pages, 2135 KiB  
Article
Reduction in Irradiation Dose in Aperture Coded Enhanced Computed Tomography Imager Using Super-Resolution Techniques
by Yossef Danan, Doron Avraham and Zeev Zalevsky
Sensors 2020, 20(22), 6551; https://doi.org/10.3390/s20226551 - 16 Nov 2020
Cited by 3 | Viewed by 1725
Abstract
One of the main concerns regarding medical imaging is the danger tissue’s ionizing due to the applied radiation. Many medical procedures are based on this ionizing radiation (such as X-rays and Gamma radiation). This radiation allows the physician to perform diagnosis inside the [...] Read more.
One of the main concerns regarding medical imaging is the danger tissue’s ionizing due to the applied radiation. Many medical procedures are based on this ionizing radiation (such as X-rays and Gamma radiation). This radiation allows the physician to perform diagnosis inside the human body. Still, the main concern is stochastic effects to the DNA, particularly the cause of cancer. The radiation dose endangers not only the patient but also the medical staff, who might be close to the patient and be exposed to this dangerous radiation in a daily manner. This paper presents a novel concept of radiation reduced Computed Tomography (CT) scans. The proposed concept includes two main methods: minification to enhance the energy concertation per pixel and subpixel resolution enhancement, using shifted images, to preserve resolution. The proposed process uses several pinhole masks as the base of the imaging modality. The proposed concept was validated numerically and experimentally and has demonstrated the capability of reducing the radiation efficiency by factor 4, being highly significant to the world of radiology and CT scans. This dose reduction allows a safer imaging process for the patient and the medical staff. This method simplifies the system and improves the obtained image quality. The proposed method can contribute additively to standard existing dose reduction or super-resolution techniques to achieve even better performance. Full article
(This article belongs to the Special Issue Lensless Imaging and Computational Sensing)
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13 pages, 2709 KiB  
Article
Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy
by Pasquale Memmolo, Pierluigi Carcagnì, Vittorio Bianco, Francesco Merola, Andouglas Goncalves da Silva Junior, Luis Marcos Garcia Goncalves, Pietro Ferraro and Cosimo Distante
Sensors 2020, 20(21), 6353; https://doi.org/10.3390/s20216353 - 07 Nov 2020
Cited by 26 | Viewed by 3561
Abstract
Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted [...] Read more.
Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition. Full article
(This article belongs to the Special Issue Lensless Imaging and Computational Sensing)
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11 pages, 3540 KiB  
Article
Lensless Computational Imaging Technology Using Deep Convolutional Network
by Peidong Chen, Xiuqin Su, Muyuan Liu and Wenhua Zhu
Sensors 2020, 20(9), 2661; https://doi.org/10.3390/s20092661 - 06 May 2020
Cited by 11 | Viewed by 2958
Abstract
Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. [...] Read more.
Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction. Full article
(This article belongs to the Special Issue Lensless Imaging and Computational Sensing)
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Review

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19 pages, 5180 KiB  
Review
Speckle-Correlation Scattering Matrix Approaches for Imaging and Sensing through Turbidity
by YoonSeok Baek, KyeoReh Lee, Jeonghun Oh and YongKeun Park
Sensors 2020, 20(11), 3147; https://doi.org/10.3390/s20113147 - 02 Jun 2020
Cited by 11 | Viewed by 4865
Abstract
The development of optical and computational techniques has enabled imaging without the need for traditional optical imaging systems. Modern lensless imaging techniques overcome several restrictions imposed by lenses, while preserving or even surpassing the capability of lens-based imaging. However, existing lensless methods often [...] Read more.
The development of optical and computational techniques has enabled imaging without the need for traditional optical imaging systems. Modern lensless imaging techniques overcome several restrictions imposed by lenses, while preserving or even surpassing the capability of lens-based imaging. However, existing lensless methods often rely on a priori information about objects or imaging conditions. Thus, they are not ideal for general imaging purposes. The recent development of the speckle-correlation scattering matrix (SSM) techniques facilitates new opportunities for lensless imaging and sensing. In this review, we present the fundamentals of SSM methods and highlight recent implementations for holographic imaging, microscopy, optical mode demultiplexing, and quantification of the degree of the coherence of light. We conclude with a discussion of the potential of SSM and future research directions. Full article
(This article belongs to the Special Issue Lensless Imaging and Computational Sensing)
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Other

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13 pages, 3289 KiB  
Letter
Image Enhancement of Computational Reconstruction in Diffraction Grating Imaging Using Multiple Parallax Image Arrays
by Jae-Young Jang and Hoon Yoo
Sensors 2020, 20(18), 5137; https://doi.org/10.3390/s20185137 - 09 Sep 2020
Cited by 6 | Viewed by 2253
Abstract
This paper describes an image enhancement method of computational reconstruction for 3-D images with multiple parallax image arrays in diffraction grating imaging. A 3-D imaging system via a diffraction grating provides a parallax image array (PIA) which is a set of perspective images [...] Read more.
This paper describes an image enhancement method of computational reconstruction for 3-D images with multiple parallax image arrays in diffraction grating imaging. A 3-D imaging system via a diffraction grating provides a parallax image array (PIA) which is a set of perspective images of 3-D objects. The parallax images obtained from diffraction grating imaging are free from optical aberrations such as spherical aberrations that are always involved in the 3-D imaging via a lens array. The diffraction grating imaging system for 3-D imaging also can be made at a lower cost system than a camera array system. However, the parallax images suffer from the speckle noise due to a coherent source; also, the noise degrades image quality in 3-D imaging. To remedy this problem, we propose a 3-D computational reconstruction method based on multiple parallax image arrays which are acquired by moving a diffraction grating axially. The proposed method consists of a spatial filtering process for each PIA and an overlapping process. Additionally, we provide theoretical analyses through geometric and wave optics. Optical experiments are conducted to evaluate our method. The experimental results indicate that the proposed method is superior to the existing method in 3-D imaging using a diffraction grating. Full article
(This article belongs to the Special Issue Lensless Imaging and Computational Sensing)
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