Juan Felipe Pérez-Juste Abascal, Ph.D.
Researcher in medical imaging, image processing, inverse problems and deep learning
Current position as Data Scientist Innovation at Groupe SEB, Lyon, France
juanabascal78 at gmail.com
Deep learning for image reconstruction
Repository:
https://github.com/openspyrit/spyrit
Contribute to SPyRiT, a PyTorch-based toolbox for deep image reconstruction.
Image reconstruction: tutorials, colab
ResPr-UNet-3D-Denoising-Efficient-Pipeline-TF-keras
Repository:
https://github.com/jabascal/ResPr-UNet-3D-Denoising-Efficient-Pipeline-TF-keras/
Modified U-Net architecture (ResPrU-Net) that exploits a Prior Image for 3D image denoising. It extends sparse, prior-based methods to learning approaches.
Efficient data pipelines for 3D denoising.
Comparison of different architectures (CNN, U-Net, ResPr-UNet), sparse based methods (TV, BM3D) and 2D and 3D denoising.
Assessed on kits19 challenge dataset.
Code and results for the paper A residual U-Net network with image prior for 3D image denoising, Proc. Eur. Signal Process. Conf. EUSIPCO, pp. 1264-1268, 2020. (hal-02500664)
