Computational Optical Imaging: Progress and Future Prospects

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1019

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Automation, Shanghai University, Shanghai 200444, China
Interests: computational optical imaging; super-resolution optical imaging; single-molecule tracking technology; deep learning for target tracking; deep learning for image processing
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
Interests: computational optical imaging; neuromorphic imaging; digital holography

Special Issue Information

Dear Colleagues,

Computational optical imaging is a cutting-edge field that deeply integrates optical imaging technology with modern computing. By co-optimizing optical system design and intelligent information processing, it overcomes the physical limitations of conventional imaging in resolution, signal-to-noise ratio (SNR), field of view, speed, and dimensionality. Rather than relying solely on hardware improvements, computational optical imaging leverages techniques such as coded sensing, computational reconstruction, and deep learning to extract high-dimensional information from compressed or degraded optical signals, enabling novel capabilities like super-resolution imaging, 3D reconstruction, phase imaging, and non-line-of-sight imaging. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Computational optical imaging;
  • Super-resolution optical imaging;
  • Single-molecule tracking technology;
  • Deep learning for target tracking;
  • Deep learning for image processing;
  • Neuromorphic imaging;
  • Digital holography.

Dr. Famin Wang
Dr. Zhou Ge
Guest Editors

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Keywords

  • optical imaging
  • deep learning
  • super-resolution
  • photonics
  • image processing
  • digital holography

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Published Papers (1 paper)

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Research

18 pages, 8322 KB  
Article
Validation of a Single-Image Inverse Rendering Setup for Optical Property Estimation in Turbid Materials
by Philipp Nguyen, David Hevisov, Markus Wagner, Joachim Jelken, Florian Foschum and Alwin Kienle
Photonics 2026, 13(3), 242; https://doi.org/10.3390/photonics13030242 - 28 Feb 2026
Viewed by 512
Abstract
This work presents an experimental validation of a physics-based inverse rendering method for determining the reduced scattering and absorption coefficients of turbid materials in arbitrary shape from a single image per wavelength. Based on our previously published theoretical inverse rendering framework, we constructed [...] Read more.
This work presents an experimental validation of a physics-based inverse rendering method for determining the reduced scattering and absorption coefficients of turbid materials in arbitrary shape from a single image per wavelength. Based on our previously published theoretical inverse rendering framework, we constructed and experimentally characterised a wavelength-selective measurement setup to realise and validate the method under real acquisition conditions. By accurately modelling the spectral behaviour and angle-dependent transmission of the employed bandpass filters, we ensured a close correspondence between captured and simulated reflectance. The method was evaluated on three silicone materials, beginning with simple cube geometries and later extending to a complex Einstein bust. Relative to integrating-sphere reference data, the recovered optical properties exhibit maximum absolute errors of approximately 4–10% for reduced scattering and 5–10% for absorption for the cubes, and 16–19% and 16–22%, respectively, for the bust. Forward renderings based on the recovered coefficients achieve CIE ΔE2000 values below 1 for the cube and below 2 for the complex geometry when compared with photographs. Additionally, we demonstrated that the approach can be applied using a common commercially available RGB camera, recovering optical parameters from each RGB channel, albeit with increased errors due to the camera’s broad spectral channels. Overall, our method enables the recovery of optical properties and the creation of accurate digital twins for objects of arbitrary shape using comparatively simple hardware, including common commercially available RGB cameras. This broadens its applicability to practical scenarios such as process monitoring and digital twinning when appearance, rather than precise material parameters, is the primary focus. Full article
(This article belongs to the Special Issue Computational Optical Imaging: Progress and Future Prospects)
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