Author Archives: almansa

Context The process of acquiring interferometric hyperspectral images from airborne or satellite sensors [1,2] comprises several steps, including : Rectification of the image sequence to a common epipolar geometry to build an epipolar plane image (EPI) sequence. Decomposition of each … Continue reading →

Context In lectures 3 and 7 we explain how to model radar image noise and how to adapt non-local patch based denoising approaches (that assume additive gaussian noise) to radar images, where the noise has different characteristics. More recently, some … Continue reading →

Contexte : Les images radar ont l’avantage de pouvoir être acquises par tout temps (de jour comme de nuit et quelles que soient les conditions météorologiques). Elles présentent néanmoins un fort bruit (le bruit de speckle lié à l’imagerie cohérente). … Continue reading →

Les méthodes basées sur les coupures de graphes (graph-cuts) [1,2] sont souvent utilisées pour régulariser ou interpoler la carte de disparité en vision stéréoscopique. Néanmoins la façon dont ils ont été mis en oeuvre [3] est souvent inadaptée à la … Continue reading →

Lorsque une images est bruitée puis compressée par une quantification en ondelettes, le bruit résiduel peut prendre une forme très structurée. Ce projet vise à étudier comment des techniques très diverses comme les transformées de stabilisation de variance, le dithering … Continue reading →

In Satellite or Airborne Interferometric Hyperspectral Imaging, the Hyperspectral cube is obtained from several interferometric images which have the following structure $$w = u(1+v)$$ where $w$ is the acquired image, $u$ is the panchromatic image, and the interferometric fringes in … Continue reading →

  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 →