Heriot-Watt University
Edinburgh EH14 4ASColin Maclaurin Building CMT.19Aa.repetti@hw.ac.uk
Department of Actuarial Mathematics and Statistics School of Mathematical and Computer Sciences
Institute of Sensors, Signals, and Systems School of Engineering and Physical Sciences
Maxwell Institute for Mathematical Sciences - Edinburgh
Python codes
Primal-dual plug-and-play for computational optical imaging with a photonic lantern
C. S. Garcia, M. Larchevêque, Solal O'Sullivan, M. Van Waerebeke, R. R. Thomson, A. Repetti, and J.-C. Pesquet, A primal-dual data-driven method for computational optical imaging with a photonic lantern, Accepted for publication in PNAS Nexus, March 2024. [pdf]
D. Choudhury, D. K. McNicholl, A. Repetti, I. Gris-Sánchez, S. Li, D. B. Phillips, G. Whyte, T. A. Birks, Y. Wiaux, and R. R. Thomson. Computational optical imaging with a photonic lantern. Nature Communications, vol. 11, no. 1, pp. 5217,Oct. 2020. [pdf]
Uncertainty Quantification in CT pulmonary angiography
A. M. Rambojun, H. Komber, J. Rossdale, J. Suntharalingam, J. C. L. Rodrigues, M. J. Ehrhardt, and A. Repetti, Uncertainty Quantification in CT pulmonary angiography, Accepted for publication in PNAS Nexus, Oct. 2023. [pdf]
Distributed Block-Split Gibbs (DSGS) sampler for image restoration
P.-A. Thouvenin, A. Repetti, and P. Chainais. A Distributed Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems. Accepted for publication in Journal of Computational and Graphical Statistics, Oct. 2023 [pdf]
P.-A. Thouvenin, A. Repetti, and P. Chainais. Un algorithme MCMC distribué pour la résolution de problèmes inverses en grande dimension. In Actes du 28e colloque GRETSI, Brest, France, 3-6 Septembre 2023. [pdf]
P.-A. Thouvenin, A. Repetti, and P. Chainais. A versatile distributed MCMC algorithm for large scale inverse problems. In Proceedings of the 30th European Signal Processing Conference (EUSIPCO 2022), Belgrade, Serbia, Aug. 2022. [pdf]
Learning maximally monotone operators
J.-C. Pesquet, A. Repetti, M. Terris, and Y. Wiaux. Learning Maximally Monotone Operators for Image Recovery. SIAM Journal on Imaging Sciences, vol. 14, no. 3, pp. 1206-1237, Aug. 2021. [pdf]
M. Terris, A. Repetti, J.-C. Pesquet, and Y. Wiaux. Enhanced convergent PnP algorithms for image restoration. In Proceedings of 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 1684-1688, Anchorage, Alaska, 19-22 Sept. 2021. [pdf]
Image processing aims to extract or interpret the information contained in the observed data linked to one (or more) image(s). Most of the analysis tools are based on the formulation of an objective function and the development of suitable optimization methods. This class of approaches, qualified as variational, has become the state-of-the-art for many image processing modalities, thanks to their ability to deal with large-scale problems, their versatility allowing them to be adapted to different contexts, as well as the associated theoretical results ensuring convergence towards a solution of the finite objective function.
Slides of the course are available here