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Photonics
Special Issues
Computational Optical Imaging and Its Applications
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Computational Optical Imaging and Its Applications

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 9500

Special Issue Editors

Prof. Dr. Xiaopeng Shao

E-Mail Website
Guest Editor
1. School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
2. Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China
Interests: computational imaging; optical instrumentation, optical image processing and pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Tengfei Wu

E-Mail Website
Guest Editor
Laboratoire Kastler-Brossel, Ecole Normale Supérieure, 75005 Paris, France
Interests: speckle imaging; wavefront sensing; adaptive optics; super-resolution imaging; Raman spectroscopy

Special Issue Information

Dear Colleagues,

Computational optical imaging is an indirect method to acquire target information which may be hard to access by direct observation. On the basis of geometrical optics, computational imaging gathers more information as prior knowledge (in different cases this could be, for instance, polarization, phase, sparsity, positivity, etc.), and retrieves seemingly unreachable information by using mathematical analysis and specific signal processing algorithms. Thanks to the rapid development of hardware devices, computational optical imaging has attracted more attention in recent years and its development covers various fields, ranging from astronomical imaging to microscopic imaging. Except for the advantage of being able to obtain more information, computational optical imaging could also be low cost and simplify optical design. This Special Issue aims to highlight the latest advances in computational optical imaging, including novel concepts and interesting practical applications.

This Special Issue focuses on (but is not limited to) the following topics:

  • Imaging through scattering media;
  • Wavefront shaping and transmission matrix;
  • Deep imaging inside the tissue;
  • Non-line-of-sight imaging;
  • Super-resolution imaging;
  • Lensless imaging;
  • Polarization imaging;
  • Wavefront sensing;
  • Adaptive optics in microscopy;
  • Compressed sensing.

Prof. Dr. Xiaopeng Shao
Dr. Tengfei Wu
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. Photonics is an international peer-reviewed open access monthly 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 2400 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

  • scattering media
  • image processing and algorithm
  • microscopy
  • super-resolution imaging
  • wavefront sensing
  • lensless imaging

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

21 pages, 12246 KiB  
Article
Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging
by Hong Zeng, Jiawei Dong, Qianxi Li, Weining Chen, Sen Dong, Huinan Guo and Hao Wang
Photonics 2023, 10(10), 1109; https://doi.org/10.3390/photonics10101109 - 30 Sep 2023
Cited by 1 | Viewed by 1307
Abstract
The combination of single-pixel imaging and single photon-counting technology enables ultra-high-sensitivity photon-counting imaging. In order to shorten the reconstruction time of single-photon counting, the algorithm of compressed sensing is used to reconstruct the underdetermined image. Compressed sensing theory based on prior constraints provides [...] Read more.
The combination of single-pixel imaging and single photon-counting technology enables ultra-high-sensitivity photon-counting imaging. In order to shorten the reconstruction time of single-photon counting, the algorithm of compressed sensing is used to reconstruct the underdetermined image. Compressed sensing theory based on prior constraints provides a solution that can achieve stable and high-quality reconstruction, while the prior information generated by the network may overfit the feature extraction and increase the burden of the system. In this paper, we propose a novel sparse autoencoder network prior for the reconstruction of the single-pixel imaging, and we also propose the idea of multi-channel prior, using the fully connected layer to construct the sparse autoencoder network. Then, take the network training results as prior information and use the numerical gradient descent method to solve underdetermined linear equations. The experimental results indicate that this sparse autoencoder network prior for the single-photon counting compressed images reconstruction has the ability to outperform the traditional one-norm prior, effectively improving the reconstruction quality. Full article
(This article belongs to the Special Issue Computational Optical Imaging and Its Applications)
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12 pages, 741 KiB  
Article
Multi-Channel Visibility Distribution Measurement via Optical Imaging
by Lingye Chen, Yuyang Shui, Libang Chen, Ming Li, Jinhua Chu, Xia Shen, Yikun Liu and Jianying Zhou
Photonics 2023, 10(8), 945; https://doi.org/10.3390/photonics10080945 - 18 Aug 2023
Cited by 2 | Viewed by 1355
Abstract
Calibration of the imaging environment is an important step in computational imaging research, as it provides an assessment of the imaging capabilities of an imaging system. Visibility is an important quantity reflecting the transparency of the atmosphere. Currently, transmissometers and optical scatterometers are [...] Read more.
Calibration of the imaging environment is an important step in computational imaging research, as it provides an assessment of the imaging capabilities of an imaging system. Visibility is an important quantity reflecting the transparency of the atmosphere. Currently, transmissometers and optical scatterometers are the primary methods for visibility measurement. Transmissometers measure visibility along a single direction between the transmitter and receiver but encounter challenges in achieving optical alignment under long baseline conditions. Optical scatterometers measure the visibility within a localized area since they collect only a small volume of air. Hence, both transmissometers and optical scatterometers have limitations in accurately representing the visibility distribution of an inhomogeneous atmosphere. In this work, a multi-channel visibility distribution measurement via the optical imaging method is proposed and validated in a standard fog chamber. By calibrating the attenuation of infrared LED arrays, the visibility distribution over the entire field of view can be calculated based on the atmospheric visibility model. Due to the large angle of divergence of the LED, the need for optical alignment is eliminated. In further discussion, the key factors affecting the accuracy of visibility measurement are analyzed, and the results show that increasing the measurement baseline, increasing the dynamic range of the detector, and eliminating background light can effectively improve the accuracy of visibility measurement. Full article
(This article belongs to the Special Issue Computational Optical Imaging and Its Applications)
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16 pages, 5961 KiB  
Communication
Restoration of Atmospheric Turbulence-Degraded Short-Exposure Image Based on Convolution Neural Network
by Jiuming Cheng, Wenyue Zhu, Jianyu Li, Gang Xu, Xiaowei Chen and Cao Yao
Photonics 2023, 10(6), 666; https://doi.org/10.3390/photonics10060666 - 8 Jun 2023
Cited by 4 | Viewed by 1989
Abstract
Ground-based remote observation systems are vulnerable to atmospheric turbulence, which can lead to image degradation. While some methods can mitigate this turbulence distortion, many have issues such as long processing times and unstable restoration effects. Furthermore, the physics of turbulence is often not [...] Read more.
Ground-based remote observation systems are vulnerable to atmospheric turbulence, which can lead to image degradation. While some methods can mitigate this turbulence distortion, many have issues such as long processing times and unstable restoration effects. Furthermore, the physics of turbulence is often not fully integrated into the image reconstruction algorithms, making their theoretical foundations weak. In this paper, we propose a method for atmospheric turbulence mitigation using optical flow and convolutional neural networks (CNN). We first employ robust principal component analysis (RPCA) to extract a reference frame from the images. With the help of optical flow and the reference frame, the tilt can be effectively corrected. After correcting the tilt, the turbulence mitigation problem can be simplified as a deblurring problem. Then, we use a trained CNN to remove blur. By utilizing (i) a dataset that conforms to the turbulence physical model to ensure the restoration effect of the CNN and (ii) the efficient parallel computing of the CNN to reduce computation time, we can achieve better results compared to existing methods. Experimental results based on actual observed turbulence images demonstrate the effectiveness of our method. In the future, with further improvements to the algorithm and updates to GPU technology, we expect even better performance. Full article
(This article belongs to the Special Issue Computational Optical Imaging and Its Applications)
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11 pages, 6882 KiB  
Communication
Lensless Imaging via Blind Ptychography Modulation and Wavefront Separation
by Cheng Xu, Hui Pang, Axiu Cao, Qiling Deng, Song Hu and Huajun Yang
Photonics 2023, 10(2), 191; https://doi.org/10.3390/photonics10020191 - 10 Feb 2023
Viewed by 1696
Abstract
A novel lensless imaging approach based on ptychography and wavefront separation is proposed in this paper, which was characterized by rapid convergence and high-quality imaging. In this method, an amplitude modulator was inserted between the light source and the sample for light wave [...] Read more.
A novel lensless imaging approach based on ptychography and wavefront separation is proposed in this paper, which was characterized by rapid convergence and high-quality imaging. In this method, an amplitude modulator was inserted between the light source and the sample for light wave modulation. By laterally translating this unknown modulator to different positions, we acquired a sequence of modulated intensity images for quantitative object recovery. In addition, to effectively separate the object and modulator wavefront, a couple of diffraction patterns without modulation were recorded. Optical experiments were performed to verify the feasibility of our approach by testing a resolution plate, a phase object, and an agaricus cell. Full article
(This article belongs to the Special Issue Computational Optical Imaging and Its Applications)
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14 pages, 5171 KiB  
Article
Numerical and Monte Carlo Simulation for Polychromatic L-Shell X-ray Fluorescence Computed Tomography Based on Pinhole Collimator with Sheet-Beam Geometry
by Shuang Yang, Shanghai Jiang, Shenghui Shi, Xinyu Hu and Mingfu Zhao
Photonics 2022, 9(12), 928; https://doi.org/10.3390/photonics9120928 - 2 Dec 2022
Viewed by 1720
Abstract
X-ray fluorescence computed tomography (XFCT) has attracted wide attention due to its ability to simultaneously and nondestructively obtain structural and elemental distribution information within samples. In this paper, we presented an image system based on the pinhole collimator for the polychromatic L-shell XFCT [...] Read more.
X-ray fluorescence computed tomography (XFCT) has attracted wide attention due to its ability to simultaneously and nondestructively obtain structural and elemental distribution information within samples. In this paper, we presented an image system based on the pinhole collimator for the polychromatic L-shell XFCT to reduce time consumption and improve the detection limit. First, the imaging system model was expressed by formulas and discretized. Then, two phantoms (A and B) were scanned by numerical simulation and Monte Carlo simulation. Both phantoms with the same diameter (10 mm) and height (10 mm) were cylinders filled with PMMA, and embedded with GNP-loaded cylinders. The phantom A was inserted by six 1.5 mm-diameter cylinders with different Au concentrations ranging from 0.2% to 1.2%. The phantom B was inserted by eight cylinders with the same Au concentration (1%), but a radius ranging from 0.1 mm to 0.8 mm. Finally, the reconstruction of the XFCT images was performed using the method with and without absorption correction, respectively. The feasibility of XFCT system presented in this paper was demonstrated by the numerical simulation and the Monte Carlo simulation. The results show that absorption attenuation can be corrected by the presented method, and the contrast to noise ratio (CNR) is proportional to Au concentration but almost remains unchanged with the radius of GNP-loaded cylinders, which may provide the necessary justification for further optimization of the imaging system. Full article
(This article belongs to the Special Issue Computational Optical Imaging and Its Applications)
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