Category Archives: Projets2016

  Supervision Andrés Almansa Programming Language Matlab to use the current implementation of the additive model Language of choice to implement de multiplicative model References Ferrec, Y., Taboury, J., Sauer, H., Chavel, P., Fournet, P., Coudrain, C., … Primot, J. … Continue reading →

In this course we have used several kinds of regularization for the regularization of ill-posed inverse problems in satellite imaging: Tikhonov, Total Variation, sparsity-based and patch-based non-local regularizers. Convolutional neural networks have recently gained a lot of attention because of their … Continue reading →

  Supervision Andrés Almansa Programming Language C/C++ to use and modify the ipol code Language of choice (matlab, python, c++) to implement a simple variant) Bibliographic References Duran, J., Buades, A., Coll, B., & Sbert, C. (2014). A Nonlocal Variational … Continue reading →

The MIDAS instrument used for the SMOS mission is presented in the course as an example of interferometric remote-sensing device using a hexagonal sampling grid. The instrument aims to measure the earth’s brightness temperature for environmental applications, but these measurements … Continue reading →

Both in stereoscopy and in non-local image denoising a measure is required to determine to what an extent two image patches are similar despite noise and natural image variations.In this course two statistical approaches were presented to compare image patches: … Continue reading →

Encadrants : Jean-Marie Nicolas jean-marie.nicolas@telecom-paristech.fr, Florence Tupin florence.tupin@telecom-paristech.fr The objective of this project is to compare distances between distributions. Among the different metrics that are usually used to compare pdf we can cite Kullback-Leibler divergence, Kolmogorov-Smirnov test, Khi-deux test, …. … Continue reading →

Supervisor : Florence Tupin Email : florence.tupin@telecom-paristech.fr Context : SAR images are acquired by satellite or aerial sensors in all-weather and all-time conditions. Being a coherent imaging system, these images are highly noisy and difficult to segment. The aim of … Continue reading →