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Recent Innovations in Computational Imaging and Sensing
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Recent Innovations in Computational Imaging and Sensing

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

Deadline for manuscript submissions: closed (25 April 2026) | Viewed by 5059

Special Issue Editor

Dr. Rakib Hyder

E-Mail Website
Guest Editor
Innopeak Technology, Palo Alto, CA, USA
Interests: generative models; deep learning; multi-task learning; computational imaging and sensing; computer vision; computer graphics; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of computational imaging and sensing has become a significant and widely explored area of research in recent times. Across various applications, similar challenges arise that demand advanced mathematical, statistical, and algorithmic methods. This vibrant field thrives at the intersection of computational techniques and physical devices, leveraging cutting-edge tools to drive transformative progress in imaging speed, signal clarity, and data processing. The interplay between mathematical theories, physical principles, and innovative algorithms has enabled impactful advancements across fields such as X-ray imaging, optics, terahertz technology, astronomy, MRI, radar, biology, medicine, and seismic studies.

This Special Issue aims to contribute to this dynamic area ranging from foundational theoretical developments to real-world system implementations and case studies, advancing both the science and practical applications of modern imaging systems.

Potential topics include but are not limited to:

  • Computational imaging: machine learning, inverse problems, and model-based methodologies
  • Image reconstruction: recovery, denoising, super-resolution, and depth-of-field optimization, color correction, deblurring, and data reconstruction
  • Sensor-based processing: compressive sensing and phase retrieval
  • Medical and scientific imaging: tomography, MRI, molecular imaging, ultra-fast imaging, microscopy
  • Coherent imaging: holography, optical coherence, and coded aperture techniques
  • Remote sensing: radar, LIDAR, and synthetic aperture imaging systems
  • Industrial applications: imaging solutions for commercial and technical environments

Dr. Rakib Hyder
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • computational imaging
  • image reconstruction
  • machine learning
  • inverse problems
  • compressive sensing
  • medical imaging
  • coherent imaging
  • remote sensing
  • tomography
  • industrial applications

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

Published Papers (5 papers)

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Research

28 pages, 5404 KB  
Article
A High-Precision Method for Extracting Lateral Deformation in Operational Shield Tunnels Based on LiDAR Point Cloud Analysis
by Sijia Tang and Xiangyang Xu
Sensors 2026, 26(10), 3111; https://doi.org/10.3390/s26103111 - 14 May 2026
Viewed by 242
Abstract
Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between [...] Read more.
Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between adjacent sections. This paper presents a high-precision method to extract lateral deformation from tunnel LiDAR point clouds. First, a point-wise attention Transformer network (PWAT) is proposed based on PointNet++ for lining segmentation, using k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention. Second, a continuity-constrained RANSAC (CC-RANSAC) algorithm is developed to improve ellipse parameter stability by adding continuity penalties between neighboring sections. Experiments were carried out on a Shanghai metro shield tunnel. Results show that PWAT achieves 99.53% overall accuracy and 99.06% mIoU in six-class segmentation. CC-RANSAC reduces the mean residual to 2.0 mm and the center jump rate to 4.2%. Compared with total station data, the mean absolute error and root mean square error are 1.35 mm and 1.68 mm. The proposed method can automatically and accurately extract lateral deformation for operational shield tunnels. Full article
(This article belongs to the Special Issue Recent Innovations in Computational Imaging and Sensing)
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26 pages, 5605 KB  
Article
Weather-Robust Foreign Object Detection on Transmission Lines via Physics-Driven Complex Wavelet Unrolling
by Xiaoxiong Zhou, Junchi He, Cheng Cheng and Guangming Zhang
Sensors 2026, 26(10), 2942; https://doi.org/10.3390/s26102942 - 8 May 2026
Viewed by 349
Abstract
Foreign object detection during unmanned aerial vehicle (UAV) grid inspection suffers from severe visual degradation under adverse weather conditions, such as haze and heavy rain. Existing approaches often struggle to distinguish target textures from weather-induced noise, leading to critical performance drops. We propose [...] Read more.
Foreign object detection during unmanned aerial vehicle (UAV) grid inspection suffers from severe visual degradation under adverse weather conditions, such as haze and heavy rain. Existing approaches often struggle to distinguish target textures from weather-induced noise, leading to critical performance drops. We propose the Physics-Prior Complex Wavelet Unrolling Decoupling Module (PCW-UDM) to enable highly robust detection in complex environments. By leveraging the 2D dual-tree complex wavelet transform (2D-DTCWT), our method decouples degraded features into low-frequency and multi-directional high-frequency sub-bands. To tackle haze, we design a Physics-Guided Low-Frequency Dehazing (PGLD) branch that physically inverses the atmospheric scattering process. To combat rain, we introduce the LISTA-Unrolled High-Frequency Deraining (LUHD) branch, which innovatively applies deep unrolled sparse optimisation to remove directional rain streaks without distorting the structural phase. A novel spatio-temporal cross-domain consistency loss further guarantees weather-invariant feature alignment. Extensive evaluations on synthesised adverse datasets and the real-world RTTS dataset prove that our PCW-UDM-equipped network fundamentally overcomes the semantic conflicts of traditional cascaded restoration–detection paradigms, achieving state-of-the-art detection precision and robustness against extreme weather conditions. Full article
(This article belongs to the Special Issue Recent Innovations in Computational Imaging and Sensing)
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24 pages, 6166 KB  
Article
End-to-End Segmentation and Classification of Zooplankton Using Shadowgraphy and Convolutional Neural Networks
by Andrew Capalbo, Francis Letendre, Alexander Langner, Abigail Blackburn, Owen Dillahay and Michael Twardowski
Sensors 2026, 26(6), 1824; https://doi.org/10.3390/s26061824 - 13 Mar 2026
Viewed by 523
Abstract
With in situ imaging systems becoming more common, precise, and economically viable, use of these systems has grown dramatically, including both automated classification and biomass estimations. However, a rather large and overlooked portion of these efforts is reliable detection and classification of these [...] Read more.
With in situ imaging systems becoming more common, precise, and economically viable, use of these systems has grown dramatically, including both automated classification and biomass estimations. However, a rather large and overlooked portion of these efforts is reliable detection and classification of these organisms as they pass through the imaging device. This paper focuses on the development of an end-to-end classification CNN-based algorithm for marine zooplankton using the in situ Ichthyoplankton Imaging System (ISIIS-DPI) from Bellamare (La Jolla, CA, USA). Our novel approach considers many issues with automated segmentation and classification, including over-segmentation, noise segmentation, and organism size input. This allows for classifications in diverse water types, demonstrated by the comparison of three datasets created in conjunction with this project, each with very different water properties and zooplankton communities (Florida Gulf coast; Trondheimsfjord, Norway; Sargasso Sea). Our segmented image dataset contains 70,624 regions of interest (ROIs) across four organism classes—Chaetognath, Crustacean, Gelatinous, and Larvacean—with two classes dedicated to detritus. Four common network architectures—Resnet, Xception, GoogleNet, and Darknet—are trained on this dataset, with final test accuracies in the range of 95.94–96.09%. Following this initial training, a secondary level of classification is introduced. The base Gelatinous class is further divided into six groups. The same four CNN architectures are used once again, with final accuracies in the range of 86.12–90.40%, showing the ability to taxonomically classify down to the order level. The present work introduces a versatile, adaptable, scalable and autonomous segmentation and classification algorithm using niched networks mirroring taxonomy, and is fully contained in a publicly available MATLAB R2025a custom graphical user interface. Full article
(This article belongs to the Special Issue Recent Innovations in Computational Imaging and Sensing)
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25 pages, 3069 KB  
Article
DrSVision: A Machine Learning Tool for Cortical Region-Specific fNIRS Calibration Based on Cadaveric Head MRI
by Serhat Ilgaz Yöner, Mehmet Emin Aksoy, Hayrettin Can Südor, Kurtuluş İzzetoğlu, Baran Bozkurt and Alp Dinçer
Sensors 2025, 25(20), 6340; https://doi.org/10.3390/s25206340 - 14 Oct 2025
Cited by 1 | Viewed by 1139
Abstract
Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge [...] Read more.
Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation. Full article
(This article belongs to the Special Issue Recent Innovations in Computational Imaging and Sensing)
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Graphical abstract

30 pages, 10206 KB  
Article
Evaluation and Improvement of Image Aesthetics Quality via Composition and Similarity
by Xinyu Cui, Guoqing Tu, Guoying Wang, Senjun Zhang and Lufeng Mo
Sensors 2025, 25(18), 5919; https://doi.org/10.3390/s25185919 - 22 Sep 2025
Viewed by 1931
Abstract
The evaluation and enhancement of image aesthetics play a pivotal role in the development of visual media, impacting fields including photography, design, and computer vision. Composition, a key factor shaping visual aesthetics, significantly influences an image’s vividness and expressiveness. However, existing image optimization [...] Read more.
The evaluation and enhancement of image aesthetics play a pivotal role in the development of visual media, impacting fields including photography, design, and computer vision. Composition, a key factor shaping visual aesthetics, significantly influences an image’s vividness and expressiveness. However, existing image optimization methods face practical challenges: compression-induced distortion, imprecise object extraction, and cropping-caused unnatural proportions or content loss. To tackle these issues, this paper proposes an image aesthetic evaluation with composition and similarity (IACS) method that harmonizes composition aesthetics and image similarity through a unified function. When evaluating composition aesthetics, the method calculates the distance between the main semantic line (or salient object) and the nearest rule-of-thirds line or central line. For images featuring prominent semantic lines, a modified Hough transform is utilized to detect the main semantic line, while for images containing salient objects, a salient object detection method based on luminance channel salience features (LCSF) is applied to determine the salient object region. In evaluating similarity, edge similarity measured by the Canny operator is combined with the structural similarity index (SSIM). Furthermore, we introduce a Framework for Image Aesthetic Evaluation with Composition and Similarity-Based Optimization (FIACSO), which uses semantic segmentation and generative adversarial networks (GANs) to optimize composition while preserving the original content. Compared with prior approaches, the proposed method improves both the aesthetic appeal and fidelity of optimized images. Subjective evaluation involving 30 participants further confirms that FIACSO outperforms existing methods in overall aesthetics, compositional harmony, and content integrity. Beyond methodological contributions, this study also offers practical value: it supports photographers in refining image composition without losing context, assists designers in creating balanced layouts with minimal distortion, and provides computational tools to enhance the efficiency and quality of visual media production. Full article
(This article belongs to the Special Issue Recent Innovations in Computational Imaging and Sensing)
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