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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: 25 April 2026 | Viewed by 1334

Special Issue Editor


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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

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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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Published Papers (2 papers)

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Research

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
Viewed by 333
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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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 534
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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