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Photonics
Special Issues
Computational Imaging: Progress and Challenges
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Computational Imaging: Progress and Challenges

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

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 1813

Special Issue Editors

Prof. Dr. Haofeng Hu

E-Mail Website
Guest Editor
China School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, China
Interests: polarimetric imaging; polarimetry; deep learning; ocean optics
Special Issues, Collections and Topics in MDPI journals
Dr. Pingli Han

E-Mail Website
Guest Editor
School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
Interests: computational imaging; polarization imaging
Dr. Bofan Song

E-Mail Website
Guest Editor
Wyant College of Optical Sciences, University of Arizona, Tucson, AZ, USA
Interests: optical imaging for point-of-care diagnostics; deep learning; computational imaging

Special Issue Information

Dear Colleagues,

Computational Imaging Technology refers to a novel imaging method. It is different from the“what you see is what you get” information acquisition and processing methods of traditional optical imaging. With the development of new optoelectric devices and computing capabilities, the technology is experiencing significant growth. By using Computational Imaging Technology, we can subsequently receive abundant light field information. Indeed, information utilization and interpretation capability can be superior to traditional imaging, since it finally enables the realization of the “higher (resolution), longer (detection range), and larger (optical field of view)” requirements of photoelectric imaging.

This Special Issue invites manuscripts that explore the recent advances in “Computational imaging”. All theoretical, numerical, and experimental papers and reviews are welcome. Topics include, but are not limited to, the following:

  • Principles and theories of computational imaging;
  • Scattering imaging and non-field-of-view imaging;
  • Three-dimensional imaging;
  • Polarization imaging;
  • Holographic imaging;
  • Computational spectral imaging;
  • Single photon imaging;
  • Micronano Optics and computational imaging;
  • Biomedicine and computational imaging;
  • Artificial intelligence and computational imaging;
  • Frontier problems in computational imaging.

Prof. Dr. Haofeng Hu
Dr. Pingli Han
Dr. Bofan Song
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.

Published Papers (2 papers)

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Research

12 pages, 1505 KiB  
Article
Adaptive Ghost Imaging Based on 2D-Haar Wavelets
by Zhuo Yu, Xiaoqian Wang, Chao Gao, Huan Zhao, Hong Wang and Zhihai Yao
Photonics 2024, 11(4), 361; https://doi.org/10.3390/photonics11040361 - 12 Apr 2024
Viewed by 410
Abstract
To improve the imaging speed of ghost imaging and ensure the accuracy of the images, an adaptive ghost imaging scheme based on 2D-Haar wavelets has been proposed. This scheme is capable of significantly retaining image information even under under-sampling conditions. By comparing the [...] Read more.
To improve the imaging speed of ghost imaging and ensure the accuracy of the images, an adaptive ghost imaging scheme based on 2D-Haar wavelets has been proposed. This scheme is capable of significantly retaining image information even under under-sampling conditions. By comparing the differences in light intensity distribution and sampling characteristics between Hadamard and 2D-Haar wavelet illumination patterns, we discovered that the lateral and longitudinal information detected by the high-frequency 2D-Haar wavelet measurement basis could be used to predictively adjust the diagonal measurement basis, thereby reducing the number of measurements required. Simulation and experimental results indicate that this scheme can still achieve high-quality imaging results with about a 25% reduction in the number of measurements. This approach provides a new perspective for enhancing the efficiency of computational ghost imaging. Full article
(This article belongs to the Special Issue Computational Imaging: Progress and Challenges)
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15 pages, 2426 KiB  
Article
Lensless Image Restoration Based on Multi-Stage Deep Neural Networks and Pix2pix Architecture
by Muyuan Liu, Xiuqin Su, Xiaopeng Yao, Wei Hao and Wenhua Zhu
Photonics 2023, 10(11), 1274; https://doi.org/10.3390/photonics10111274 - 17 Nov 2023
Cited by 2 | Viewed by 1089
Abstract
Lensless imaging represents a significant advancement in imaging technology, offering unique benefits over traditional optical systems due to its compact form factor, ideal for applications within the Internet of Things (IoT) ecosystem. Despite its potential, the intensive computational requirements of current lensless imaging [...] Read more.
Lensless imaging represents a significant advancement in imaging technology, offering unique benefits over traditional optical systems due to its compact form factor, ideal for applications within the Internet of Things (IoT) ecosystem. Despite its potential, the intensive computational requirements of current lensless imaging reconstruction algorithms pose a challenge, often exceeding the resource constraints typical of IoT devices. To meet this challenge, a novel approach is introduced, merging multi-level image restoration with the pix2pix generative adversarial network architecture within the lensless imaging sphere. Building on the foundation provided by U-Net, a Multi-level Attention-based Lensless Image Restoration Network (MARN) is introduced to further augment the generator’s capabilities. In this methodology, images reconstructed through Tikhonov regularization are perceived as degraded images, forming the foundation for further refinement via the Pix2pix network. This process is enhanced by incorporating an attention-focused mechanism in the encoder--decoder structure and by implementing stage-wise supervised training within the deep convolutional network, contributing markedly to the improvement of the final image quality. Through detailed comparative evaluations, the superiority of the introduced method is affirmed, outperforming existing techniques and underscoring its suitability for addressing the computational challenges in lensless imaging within IoT environments. This method can produce excellent lensless image reconstructions when sufficient computational resources are available, and it consistently delivers optimal results across varying computational resource constraints. This algorithm enhances the applicability of lensless imaging in applications such as the Internet of Things, providing higher-quality image acquisition and processing capabilities for these domains. Full article
(This article belongs to the Special Issue Computational Imaging: Progress and Challenges)
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