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Photonics
Special Issues
Advances and Applications in Computational Imaging
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Advances and Applications in Computational Imaging

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

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 10844

Special Issue Editors

Prof. Dr. Wenlin Gong

E-Mail Website
Guest Editor
School of Optoelectronic Science and Engineering, Soochow University, Suzhou 215006, China
Interests: ghost imaging; lidar; imaging system; image reconstruction; computational imaging; light modulation; microscopy
Dr. Wen Chen

E-Mail Website
Guest Editor
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: optical encoding; information photonics; free-space optical transmission; optical imaging/sensing; single-pixel imaging; digital holography; information optics; optical signal/image processing; deep learning in optics and photonics
Special Issues, Collections and Topics in MDPI journals
Dr. Dongfeng Shi

E-Mail Website
Guest Editor
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: single-pixel imaging; computational imaging theory and reconstruction algorithms; computational spectral imaging; lidar

Special Issue Information

Dear Colleagues,

In recent years, computational imaging technology has become one of the hottest research directions in the field of imaging and sensing, and great advances have been made. Many theories and experiments have proved that computational imaging technology has high efficiency in information extraction, as well as good anti-interference. At present, the proof-of-principle verification of this technology from X-ray to microwave band has been realized, and some unique advantages such as far-field super-resolution imaging, high-resolution imaging through scattering medium, and non-line-of-sight imaging have also been demonstrated. This technology has important application prospects in remote sensing, automatic driving, industrial production and detection, life science and medical diagnosis, national defense security and other fields. However, there are still many problems to be urgently solved for the practical applications. Moreover, computational imaging is a product of interdisciplinary integration, which will further promote the development of physics, mathematics, information science, computer science and other disciplines.

This Special Issue aims to present the latest advances and applications in computational imaging; original research articles and reviews are welcomed. Research areas may include (but are not limited to) the following:

  • Single-pixel imaging.
  • Ghost imaging.
  • Computational holography.
  • Single-photon imaging.
  • Computational imaging theory and reconstruction algorithms.
  • Computational spectral imaging.
  • Non-line-of-sight imaging.
  • Scattering imaging.
  • Coded imaging.
  • Light field manipulation.
  • Signal processing for imaging and sensing.

We look forward to receiving your contributions.

Prof. Dr. Wenlin Gong
Dr. Wen Chen
Dr. Dongfeng Shi
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

  • imaging systems
  • image processing
  • coding and sensing
  • image reconstruction algorithms
  • holography

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

Published Papers (6 papers)

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Research

17 pages, 62119 KiB  
Article
The Block Landweber Iterative Method for Light Field Reconstruction from a Focal Stack
by Yuhan Liu, Gangrong Qu and Shan Gao
Photonics 2023, 10(11), 1219; https://doi.org/10.3390/photonics10111219 - 31 Oct 2023
Viewed by 1135
Abstract
Light field imaging involves reconstructing a 4D light field from a 3D focal stack, which makes it challenging to reconstruct the light field from incomplete projection data. To address this problem, a linear projection system is established to model the focal stack imaging [...] Read more.
Light field imaging involves reconstructing a 4D light field from a 3D focal stack, which makes it challenging to reconstruct the light field from incomplete projection data. To address this problem, a linear projection system is established to model the focal stack imaging process using discrete refocusing equations. Based on this system, we propose the block Landweber iterative method to find the least-squares solution. This method computes the sparse matrix while iterating, which overcomes the problem of data storage. The 2-norm of the block matrix is utilized as the weighted matrix to normalize every block matrix on an identical scale, delivering an effective relaxation strategy under the convergence condition in the inconsistent case, which yields better reconstruction results and accelerates the convergence speed. The experimental results based on the image quality assessments of reference and non-reference images show that our method achieved better reconstruction results compared to other relevant common methods, even with fewer focal stacks and higher angle resolution. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Imaging)
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14 pages, 4912 KiB  
Article
Enhanced Deconvolution and Denoise Method for Scattering Image Restoration
by Zepeng Chen, Haolin Wu, Wenyong Li and Jiahui Wang
Photonics 2023, 10(7), 751; https://doi.org/10.3390/photonics10070751 - 29 Jun 2023
Cited by 2 | Viewed by 1564
Abstract
Light scattering is a common physical phenomenon in nature. The scattering medium will randomly change the direction of incident light propagation, making it difficult for traditional optical imaging methods to detect objects behind the scattering body. Wiener filtering deconvolution technology based on the [...] Read more.
Light scattering is a common physical phenomenon in nature. The scattering medium will randomly change the direction of incident light propagation, making it difficult for traditional optical imaging methods to detect objects behind the scattering body. Wiener filtering deconvolution technology based on the optical memory effect has broad application prospects by virtue of its advantages, such as fast calculation speed and low cost. However, this method requires manual parameter adjustment, which is inefficient and cannot deal with the impact of real-scene noise. This paper proposes an improved Wiener filtering deconvolution method that improves the exposure dose during the speckle collection, can quickly obtain the optimal parameter during the calculation phase, and can be completed within 41.5 ms (for a 2448 × 2048 image). In addition, a neural network denoising model was proposed to address the noise issue in the deconvolution recovery results, resulting in an average improvement of 27.3% and 186.7% in PSNR and SSIM of the images, respectively. The work of this paper will play a role in achieving real-time high-quality imaging of scattering media and be helpful in studying the physical mechanisms of scattering imaging. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Imaging)
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10 pages, 974 KiB  
Communication
High-Quality Computational Ghost Imaging with a Conditional GAN
by Ming Zhao, Xuedian Zhang and Rongfu Zhang
Photonics 2023, 10(4), 353; https://doi.org/10.3390/photonics10040353 - 23 Mar 2023
Cited by 2 | Viewed by 1866
Abstract
In this study, we demonstrated a framework for improving the image quality of computational ghost imaging (CGI) that used a conditional generative adversarial network (cGAN). With a set of low-quality images from a CGI system and their corresponding ground-truth counterparts, a cGAN was [...] Read more.
In this study, we demonstrated a framework for improving the image quality of computational ghost imaging (CGI) that used a conditional generative adversarial network (cGAN). With a set of low-quality images from a CGI system and their corresponding ground-truth counterparts, a cGAN was trained that could generate high-quality images from new low-quality images. The results showed that compared with the traditional method based on compressed sensing, this method greatly improved the image quality when the sampling ratio was low. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Imaging)
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14 pages, 4931 KiB  
Article
Optimization of Sampling Mode in Macro Fourier Ptychography Imaging Based on Energy Distribution
by Runbo Jiang, Dongfeng Shi and Yingjian Wang
Photonics 2023, 10(3), 321; https://doi.org/10.3390/photonics10030321 - 16 Mar 2023
Viewed by 1581
Abstract
Fourier ptychography imaging technology is a method developed in recent years to achieve high-resolution imaging. In the traditional macro Fourier ptychography technology, the scanning method when the camera captures low-resolution images mostly uses the rectangular linear grid format. These acquired images contain a [...] Read more.
Fourier ptychography imaging technology is a method developed in recent years to achieve high-resolution imaging. In the traditional macro Fourier ptychography technology, the scanning method when the camera captures low-resolution images mostly uses the rectangular linear grid format. These acquired images contain a small amount of complementary information, and a large number of low-resolution images are needed to achieve high-resolution imaging. Redundant measurements will extend the sampling and reconstruction time, and require more computing resources. In this paper, we propose to obtain the target image spectral energy distribution by pre-sampling. And according to the energy distribution, we use irregular and non-uniform sampling modes to restore the target image. With the same number of samples and same reconstruction time, higher resolution imaging can be achieved compared with traditional methods. Simulation and experimental studies are carried out in this paper, and the results confirm the effectiveness of the proposed methods. Compared with the traditional sampling mode, the two sampling modes proposed in this paper increase the resolution from 4.49 lp/mm to 5.66 lp/mm and 5.04 lp/mm respectively. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Imaging)
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14 pages, 5977 KiB  
Article
Turbulence Aberration Restoration Based on Light Intensity Image Using GoogLeNet
by Huimin Ma, Weiwei Zhang, Xiaomei Ning, Haiqiu Liu, Pengfei Zhang and Jinghui Zhang
Photonics 2023, 10(3), 265; https://doi.org/10.3390/photonics10030265 - 2 Mar 2023
Cited by 4 | Viewed by 1962
Abstract
Adaptive optics (AO) is an effective method to compensate the wavefront distortion caused by atmospheric turbulence and system distortion. The accuracy and speed of aberration restoration are important factors affecting the performance of adaptive optics correction. In recent years, an AO correction method [...] Read more.
Adaptive optics (AO) is an effective method to compensate the wavefront distortion caused by atmospheric turbulence and system distortion. The accuracy and speed of aberration restoration are important factors affecting the performance of adaptive optics correction. In recent years, an AO correction method based on a convolutional neural network (CNN) has been proposed for the non-iterative extraction of light intensity image features and recovery of phase information. This method can directly predict the Zernike coefficient of the wavefront from the measured light intensity image and effectively improve the real-time correction ability of the AO system. In this paper, a turbulence aberration restoration based on two frames of a light intensity image using GoogLeNet is established. Three depth scales of GoogLeNet and different amounts of data training are tested to verify the accuracy of Zernike phase difference restoration at different turbulence intensities. The results show that the training of small data sets easily overfits the data, while the training performance of large data sets is more stable and requires a deeper network, which is conducive to improving the accuracy of turbulence aberration restoration. The restoration effect of third-order to seventh-order aberrations is significant under different turbulence intensities. With the increase in the Zernike coefficient, the error increases gradually. However, there are valley points lower than the previous growth for the 10th-, 15th-, 16th-, 21st-, 28th- and 29th-order aberrations. For higher-order aberrations, the greater the turbulence intensity, the greater the restoration error. The research content of this paper can provide a network design reference for turbulence aberration restoration based on deep learning. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Imaging)
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15 pages, 4621 KiB  
Article
Sampling and Reconstruction Jointly Optimized Model Unfolding Network for Single-Pixel Imaging
by Qiurong Yan, Xiancheng Xiong, Ke Lei, Yongjian Zheng and Yuhao Wang
Photonics 2023, 10(3), 232; https://doi.org/10.3390/photonics10030232 - 21 Feb 2023
Cited by 3 | Viewed by 1812
Abstract
In recent years, extensive research has shown that deep learning-based compressed image reconstruction algorithms can achieve faster and better high-quality reconstruction for single-pixel imaging, and that reconstruction quality can be further improved by joint optimization of sampling and reconstruction. However, these network-based models [...] Read more.
In recent years, extensive research has shown that deep learning-based compressed image reconstruction algorithms can achieve faster and better high-quality reconstruction for single-pixel imaging, and that reconstruction quality can be further improved by joint optimization of sampling and reconstruction. However, these network-based models mostly adopt end-to-end learning, and their structures are not interpretable. In this paper, we propose SRMU-Net, a sampling and reconstruction jointly optimized model unfolding network. A fully connected layer or a large convolutional layer that simulates compressed reconstruction is added to the compressed reconstruction network, which is composed of multiple cascaded iterative shrinkage thresholding algorithm (ISTA) unfolding iteration blocks. To achieve joint optimization of sampling and reconstruction, a specially designed network structure is proposed so that the sampling matrix can be input into ISTA unfolding iteration blocks as a learnable parameter. We have shown that the proposed network outperforms the existing algorithms by extensive simulations and experiments. Full article
(This article belongs to the Special Issue Advances and Applications in Computational Imaging)
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