Hoang Trieu Vy LE

I'm currently a Principal AI Research And Development Engineer at BeinkDream, where I mainly work on computer visions tasks and generative AI.

I received the Ph.D degree in Signal & Image Processing from ENS Lyon in 2023 under the warm and insightful supervision of Nelly Pustelnik and Marion Foare.

My research bridges mathematical optimization and deep learning — designing structured neural architectures that incorporate mathematical priors to solve inverse problems and learn reliable representations of the world.

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Ph.D Thesis

Supervisors: Dr. Nelly Pustelnik, Dr. Marion Foare

Reviewers: Prof. Caroline Chaux, Assoc. Prof. Matthieu Kowalski

Examiners: Prof. Jean-François Aujol, Prof. Elie Bretin, Prof. Luca Calatroni, Prof. Marc Sebban

Publications

My research topics include unfolded Neural Networks, computer vision , deep learning, generative AI, and image processing.

We propose an unfolded neural network approach for simultaneous image denoising and contour detection, bridging variational procedures designed to perform the combined denoising/edge detection task and black-box neural networks designed for edge detection.

In this paper, we present a new edge detection model based on proximal unfolded neural networks. The architecture relies on unfolding proximal Blake-Zisserman iterations, leading to a composition of two blocks: a smoothing block and an edge detection block.

@ARTICLE{10630640,
  author={Le, Hoang Trieu Vy and Repetti, Audrey and Pustelnik, Nelly},
  journal={IEEE Transactions on Image Processing}, 
  title={Unfolded Proximal Neural Networks for Robust Image Gaussian Denoising}, 
  year={2024},
  volume={33},
  number={},
  pages={4475-4487},
  doi={10.1109/TIP.2024.3437219}}

We propose different learning strategies for our PNN framework, and investigate their robustness (Lipschitz property) and denoising efficiency. Finally, we assess the robustness of our PNNs when plugged in a forward-backward algorithm for an image deblurring problem

@ARTICLE{9723590,
  author={Le, Hoang Trieu Vy and Foare, Marion and Pustelnik, Nelly},
  journal={IEEE Signal Processing Letters}, 
  title={Proximal Based Strategies for Solving Discrete Mumford-Shah 
        With Ambrosio-Tortorelli Penalization on Edges}, 
  year={2022},
  volume={29},
  number={},
  pages={952-956},
  doi={10.1109/LSP.2022.3155307}}

This work is dedicated to joint image restoration and contour detection considering the Ambrosio-Tortorelli functional. Two proximal alternating minimization schemes with convergence guarantees are provided, PALM-AT and SL-PAM-AT, as well as closed-form expressions of the involved proximity operators.

@INPROCEEDINGS{9909592,
  author={Le, Hoang Trieu Vy and Pustelnik, Nelly and Foare, Marion},
  booktitle={2022 30th European Signal Processing Conference (EUSIPCO)}, 
  title={The faster proximal algorithm, the better unfolded deep learning architecture ? 
    The study case of image denoising}, 
  year={2022},
  pages={947-951},
  doi={10.23919/EUSIPCO55093.2022.9909592}}

In this work, we proposed two deep unfolded networks for gaussian denoising that can be activated from standard or accelerated schemes to illustrate the benefit to unroll accelerated schemes when possible.

@inproceedings{HoangTVL2022AlgorithmesPR,
  title={Fast Proximal Unrolled Algorithms for the Analysis of Piecewise Homogeneous Fractal Images},
  author={ Hoang T.V Le, Barbara Pascal, Nelly Pustelnik, Marion Foare and Patrice Abry},
  year={2022},
  url={https://api.semanticscholar.org/CorpusID:253115164}
}

We propose two unrolled deep network architectures built from the proximal FISTA and Chambolle-Pock algorithms to estimate local regularity in piecewise homogeneous fractal images.

Talks

Teaching