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Latest projects

 

 

 

Lately, I have had the opportunity to work of the following challenging problems: robust deep iterative reconstruction, high-dimensional image denoising and image restoration, motion artefact reduction, object detection, NLP and ChatGPT prompt text analysis.   ​

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Deep high-dimensional denoising

2D+frequency denoising:

  • Exploits higher-order convolutions (regularization across both space and frequency)

  • Image denoising for single-pixel hyperspectral imaging

 

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Transfer learning

  • Learned the detector response function for spectral CT from few datasets

  • A Sim2Real transfer learning approach when a ground truth is not available

  • Material decomposition of human thorax data

  • Improved regularized approaches

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Deep residual prior denoising

State-of-the art denoising models

  • ResNet, U-Net architecture, ViT

  • Specifically designed architectures

Deep learning denoising with Prior Image:

  • 2D and 3D image denoising of spectral CT images

  • Exploits a Prior image (extending sparse and prior-based methods)

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Sparse denoising methods

Sparse spatial and prior based denoising (SPADE) method

  • 3D image denoising

  • Exploits a Prior Image

  • Improved stability and convergence

  • Reduces cartoon-like artefacts

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Deep iterative reconstruction
  • Robust methods for medical imaging (ensure data fidelity)

  • Unrolled methods

  • Plug-and-play iterative methods based on state-of-the art denoising

Computer vision
  • Video object detection with TFLite on Raspberry Pi

  • Intelligent video surveillance

  • Image and video restoration

  • Semantic segmentation

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Photon counting Spectral CT
  • EU-H2020 Marie Skłodowska-Curie Project "SUCCESS" on spectral computed tomography (CT)

  • Material decomposition for ​spectral CT 

     

    • Regularization methods

    • Deep learning

    • Feasibility of early detection of Osteoarthritis ​using spectral CT

NLP
  • Named-entity recognition

  • Text classification

  • OCR

  • ChatGPT prompt text analysis

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