Emerging Domains in Computational Imaging and Computational Photography

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computational Imaging and Computational Photography".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 575

Special Issue Editor


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Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: deep learning; medical imaging; image processing; computer vision
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Special Issue Information

Dear Colleagues,

The rapid evolution of computational imaging and computational photography has revolutionized how we capture, process, and interpret visual information. By synergizing advanced algorithms, novel optical designs, and data-driven methodologies, these fields are transcending the traditional boundaries of imaging systems. Recent advancements in deep learning, neural rendering, and physics-inspired models have further accelerated progress, enabling unprecedented capabilities in image reconstruction, enhancement, and semantic understanding.

Emerging applications span diverse domains, including medical imaging (e.g., accelerated MRI/CT reconstruction, low-dose imaging, and microscopic image analysis), autonomous systems, augmented/virtual reality, remote sensing, and industrial inspection. In medical imaging, computational techniques are addressing critical challenges such as artifact reduction, super-resolution reconstruction, and automated diagnosis, while deep learning frameworks are reshaping image acquisition pipelines and enabling real-time analysis. Beyond healthcare, innovations in event-based sensing, non-line-of-sight imaging, and multi-modal fusion are pushing the frontiers of visual perception.

This Special Issue invites contributions that explore cutting-edge methodologies, interdisciplinary applications, and theoretical insights in computational imaging and photography. Topics of interest include, but are not limited to, image reconstruction/analysis, physics-informed neural networks, generative models for image synthesis, computational optics, and ethical considerations in AI-driven imaging.

Dr. Rongjun Ge
Guest Editor

Manuscript Submission Information

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Keywords

  • computer vision
  • deep learning/machine learning
  • computational imaging
  • computational photography
  • medical image reconstruction/analysis
  • neural rendering
  • multi-modal imaging
  • image enhancement/segmentation/detection
  • generative models
  • AI-driven imaging

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Published Papers (1 paper)

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Research

23 pages, 4380 KB  
Article
Vision-Based Measurement of Breathing Deformation in Wind Turbine Blade Fatigue Test
by Xianlong Wei, Cailin Li, Zhiyong Wang, Zhao Hai, Jinghua Wang and Leian Zhang
J. Imaging 2026, 12(4), 174; https://doi.org/10.3390/jimaging12040174 - 17 Apr 2026
Viewed by 232
Abstract
Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing [...] Read more.
Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article
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