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Computational Optical Sensing and Imaging

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 3227

Special Issue Editors

Dr. Jun Ke

E-Mail Website
Guest Editor
Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
Interests: compressive imaging; super-resolution; single pixel imaging; IR imaging; LiDAR; NLOS
Prof. Dr. Gonzalo Arce

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Delaware, Newark, DE 19711, USA
Interests: computational imaging; LiDAR; graph signal processing
Prof. Dr. Xu Ma

E-Mail Website
Guest Editor
School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
Interests: computational imaging; computational lithography; optoelectronic image processing
Dr. Zhenbo Ren

E-Mail Website
Guest Editor
School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
Interests: computational imaging; digital holography; object detection; adaptive optics

Special Issue Information

Dear Colleagues,

Computational Optical Sensing and Imaging is a multidisciplinary area that has developed rapidly in the last two decades. From initial PSF engineering, to compressive sensing, single-pixel imaging, light field, lensless imaging, and recent event-driven and non-line-of-sight imaging, COSI has many exciting research themes. By jointly designing optical, electronic, and data processing components, a COSI system has capabilities that are not afforded by classical methods. The rapid progression of AI and nanotechnology is advancing COSI into a new stage of development. 

This Special Issue will focus on the methods, systems, and experimental techniques used to in COSI， especially the AI- and nanotechnology-assisted ones. Topics of interest are related to, but not limited by, the following: basic theories of computational imaging, computational microscopy, compressive imaging (including compressive spectral imaging), computational display for AR/VR, computational holography, event-driven computational imaging, imaging through scattering media, lensless imaging, LIDAR, light field, non-line-of-sight imaging, single-pixel imaging, structure illumination for 3D imaging, and super-resolution. 

Dr. Jun Ke
Prof. Dr. Gonzalo Arce
Prof. Dr. Xu Ma
Dr. Zhenbo Ren
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • basic theories of computational imaging
  • computational microscopy
  • compressive imaging
  • compressive spectral imaging
  • computational display for AR/VR
  • computational holography
  • event-driven computational imaging
  • imaging through scattering media
  • lensless imaging
  • LIDAR
  • light field
  • non-line-of-sight imaging
  • single-pixel imaging
  • structure illumination for 3D imaging
  • super-resolution

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Published Papers (6 papers)

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Research

14 pages, 7865 KiB  
Article
Time-Interval-Guided Event Representation for Scene Understanding
by Boxuan Wang, Wenjun Yang, Kunqi Wu, Rui Yang, Jiayue Xie and Huixiang Liu
Sensors 2025, 25(10), 3186; https://doi.org/10.3390/s25103186 - 19 May 2025
Viewed by 160
Abstract
The recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras [...] Read more.
The recovery of scenes under extreme lighting conditions is pivotal for effective image analysis and feature detection. Traditional cameras face challenges with low dynamic range and limited spectral response in such scenarios. In this paper, we advocate for the adoption of event cameras to reconstruct static scenes, particularly those in low illumination. We introduce a new method to elucidate the phenomenon where event cameras continue to generate events even in the absence of brightness changes, highlighting the crucial role played by noise in this process. Furthermore, we substantiate that events predominantly occur in pairs and establish a correlation between the time interval of event pairs and the relative light intensity of the scene. A key contribution of our work is the proposal of an innovative method to convert sparse event streams into dense intensity frames without dependence on any active light source or motion, achieving the static imaging of event cameras. This method expands the application of event cameras in static vision fields such as HDR imaging and leads to a practical application. The feasibility of our method was demonstrated through multiple experiments. Full article
(This article belongs to the Special Issue Computational Optical Sensing and Imaging)
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17 pages, 8550 KiB  
Article
Enhancing Historical Aerial Photographs: A New Approach Based on Non-Reference Metric and Photo Interpretation Elements
by Abdullah Harun Incekara and Dursun Zafer Seker
Sensors 2025, 25(7), 2126; https://doi.org/10.3390/s25072126 - 27 Mar 2025
Viewed by 268
Abstract
Deep learning-based super-resolution (SR) is an effective state-of-the-art technique for enhancing low-resolution images. This study explains a hierarchical dataset structure within the scope of enhancing grayscale historical aerial photographs with a basic SR model and relates it to non-reference image quality metric. The [...] Read more.
Deep learning-based super-resolution (SR) is an effective state-of-the-art technique for enhancing low-resolution images. This study explains a hierarchical dataset structure within the scope of enhancing grayscale historical aerial photographs with a basic SR model and relates it to non-reference image quality metric. The dataset was structured based on the hierarchy of photo interpretation elements. Images of bare land and forestry areas were evaluated as the primary category containing tone and color elements, images of residential areas as the secondary category containing shape and size elements, and images of farmland areas as the tertiary category containing pattern elements. Instead of training all images in all categories at once, which is the issue that any SR model with low number of parameters has difficulty handling, each category was trained separately. Test images containing the features of each category were enhanced separately, which means three enhanced images for one test image. The obtained images were divided into equal parts of 5 × 5 pixel size, and the final image was created by concatenating those that were determined to be of higher quality based on the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric values. Subsequently, comparative analyses based on visual interpretation and reference-based image quality metrics proved that the approach to the dataset structure positively impacted the results. Full article
(This article belongs to the Special Issue Computational Optical Sensing and Imaging)
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16 pages, 4095 KiB  
Article
Color-Coded Compressive Spectral Imager Based on Focus Transformer Network
by Jinshan Li, Xu Ma, Aanish Paruchuri, Abdullah Alrushud and Gonzalo R. Arce
Sensors 2025, 25(7), 2006; https://doi.org/10.3390/s25072006 - 23 Mar 2025
Viewed by 362
Abstract
Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the [...] Read more.
Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms. Full article
(This article belongs to the Special Issue Computational Optical Sensing and Imaging)
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13 pages, 6468 KiB  
Article
Adaptive Vectorial Restoration from Dynamic Speckle Patterns Through Biological Scattering Media Based on Deep Learning
by Yu-Chen Chen, Shi-Xuan Mi, Ya-Ping Tian, Xiao-Bo Hu, Qi-Yao Yuan, Khian-Hooi Chew and Rui-Pin Chen
Sensors 2025, 25(6), 1803; https://doi.org/10.3390/s25061803 - 14 Mar 2025
Viewed by 448
Abstract
Imaging technologies based on vector optical fields hold significant potential in the biomedical field, particularly for non-invasive scattering imaging of anisotropic biological tissues. However, the dynamic and anisotropic nature of biological tissues poses severe challenges to the propagation and reconstruction of vector optical [...] Read more.
Imaging technologies based on vector optical fields hold significant potential in the biomedical field, particularly for non-invasive scattering imaging of anisotropic biological tissues. However, the dynamic and anisotropic nature of biological tissues poses severe challenges to the propagation and reconstruction of vector optical fields due to light scattering. To address this, we propose a deep learning-based polarization-resolved restoration method aimed at achieving the efficient and accurate imaging reconstruction from speckle patterns generated after passing through anisotropic and dynamic time-varying biological scattering media. By innovatively leveraging the two orthogonal polarization components of vector optical fields, our approach significantly enhances the robustness of imaging reconstruction in dynamic and anisotropic biological scattering media, benefiting from the additional information dimension of vectorial optical fields and the powerful learning capacity of a deep neural network. For the first time, a hybrid network model is designed that integrates convolutional neural networks (CNN) with a Transformer architecture for capturing local and global features of a speckle image, enabling adaptive vectorial restoration of dynamically time-varying speckle patterns. The experimental results demonstrate that the model exhibits excellent robustness and generalization capabilities in reconstructing the two orthogonal polarization components from dynamic speckle patterns behind anisotropic biological media. This study not only provides an efficient solution for scattering imaging of dynamic anisotropic biological tissues but also advances the application of vector optical fields in dynamic scattering environments through the integration of deep learning and optical technologies. Full article
(This article belongs to the Special Issue Computational Optical Sensing and Imaging)
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19 pages, 20153 KiB  
Article
Dynamic Interferometry for Freeform Surface Measurement Based on Machine Learning-Configured Deformable Mirror
by Xu Chang, Yao Hu, Jintao Wang, Xiang Liu and Qun Hao
Sensors 2025, 25(2), 490; https://doi.org/10.3390/s25020490 - 16 Jan 2025
Cited by 1 | Viewed by 630
Abstract
Optical freeform surfaces are widely used in imaging and non-imaging systems due to their high design freedom. In freeform surface manufacturing and assembly, dynamic freeform surface measurement that can guide the next operation remains a challenge. To meet this urgent need, we propose [...] Read more.
Optical freeform surfaces are widely used in imaging and non-imaging systems due to their high design freedom. In freeform surface manufacturing and assembly, dynamic freeform surface measurement that can guide the next operation remains a challenge. To meet this urgent need, we propose a dynamic interferometric method based on a machine learning-configured deformable mirror (DM). In this method, a dynamic interferometric system is developed. By using coaxial structure and polarization interference, transient measurement of the measured surface can be realized to meet dynamic requirements, and at the same time, DM transient monitoring can be realized to reduce the accuracy loss caused by DM surface changes and meet dynamic requirements. A transient phase modulation scheme using machine learning to configure the DM surface is proposed, which keeps the system in a measurable state. Compared with the traditional phase modulation scheme that relies on iteration, the scheme proposed in this paper is more efficient and is conducive to meeting dynamic requirements. The feasibility is verified by practical experiments. The research in this paper has significance for guiding the application of dynamic interferometry in the measurement of dynamic surfaces. Full article
(This article belongs to the Special Issue Computational Optical Sensing and Imaging)
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16 pages, 7234 KiB  
Article
Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling
by Niklas Alexander Köhler, Marcel Geis, Claudius Nöh, Alexandra Mielke, Volker Groß, Robert Lange, Keywan Sohrabi and Jochen Frey
Sensors 2025, 25(1), 109; https://doi.org/10.3390/s25010109 - 27 Dec 2024
Viewed by 757
Abstract
Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that [...] Read more.
Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance. Full article
(This article belongs to the Special Issue Computational Optical Sensing and Imaging)
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