Advancements in Computational Imaging and Optical Computing

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

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 446

Special Issue Editor


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Guest Editor
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
Interests: optics; computational imaging; deep learning

Special Issue Information

Dear Colleagues,

We kindly ask you to provide us with a short summary that provides a "rationale" to enrich this Special Issue. This summary should be a guide to potential authors and should clarify the background and aims of the Special Issue. The summary can be prepared according to your research interests and wider research trends.

Computational imaging is an emerging field that seeks to push the fundamental limits in imaging systems by integrating optics and computation. These new-generation imaging systems embed computers as part of the imaging system, where optical setup and post-processing algorithms are designed simultaneously. On the other hand, recent advances in optical computing, such as all-optical neural networks, provide promising alternatives to enable highly efficient “computing” at the speed of light using only optical and photonic components. Such novel optical computing devices promise to significantly reduce power, bandwidth, and size and enable “edge computing” directly on systems. Furthermore, the amount of information that can be extracted from these images is tremendous. Researchers have leveraged advances in artificial intelligence and machine learning to translate these images to meaningful biological/histological information, as seen in techniques such as imaging modality translation and digital staining. As a cross-disciplinary research topic, computational imaging has evolved far beyond simply imaging, drawing interests from expertise in optical physics, signal processing, computer science, and machine learning, with broad applications in bioimaging, physical science, and industrial inspection.

This Special Issue aims to publish selected contributions on advances in the design, development, and applications of computational imaging and optical computing. Potential topics include, but are not limited to, the following: computational imaging, optical system designs, lens-less imaging, optical neural networks, AI-augmented imaging, optical computing, quantitative phase imaging, computational microscopy, imaging modality translation, and digital staining.

Dr. Yunzhe Li
Guest Editor

Manuscript Submission Information

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Keywords

  • computational imaging
  • optical system design lens-less imaging
  • optical neural networks
  • AI-augmented imaging
  • optical computing
  • quantitative phase imaging
  • computational microscopy
  • imaging modality translation
  • digital staining

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Published Papers (1 paper)

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12 pages, 5916 KiB  
Article
Classical Ghost Imaging with Unknowing Pseudo-Thermal Light
by Junyan Hu, Yan Guo, Binglin Chen, Yikang He, Peiming Li and Baoqing Sun
Photonics 2025, 12(5), 441; https://doi.org/10.3390/photonics12050441 - 2 May 2025
Viewed by 63
Abstract
Classical ghost imaging (CGI), an extension of quantum ghost imaging (QGI), enables object reconstruction by leveraging the spatial correlation between a pair of beams. Traditionally, CGI requires a camera or point scan to capture the spatial information of the illumination source with intensity [...] Read more.
Classical ghost imaging (CGI), an extension of quantum ghost imaging (QGI), enables object reconstruction by leveraging the spatial correlation between a pair of beams. Traditionally, CGI requires a camera or point scan to capture the spatial information of the illumination source with intensity fluctuations. In this work, we propose a novel CGI scheme that utilizes an incoherent source to illuminate both the object and the modulations, without introducing any mutual interference between them. Through theoretical analysis and experimental validation, we demonstrate that the reconstruction process relies solely on the modulations and correlation signals of two single-pixel detectors. Concurrently, this scheme is also extended to ghost diffraction, verifying the correlation between two planes that are Fourier transform pairs of the speckle field. Moreover, our study reveals the intricate relationships between the speckle field, modulations, and object, and experimentally verifies the impact of speckle fields on image quality. Notably, this work provides a more comparable framework between CGI and QGI, offering a promising avenue to explore the classical–quantum relationship. Full article
(This article belongs to the Special Issue Advancements in Computational Imaging and Optical Computing)
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