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Astronomical optical interferometers (OI) sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid atmospheric perturbations, the phases of the complex Fourier samples (visibilities) cannot be directly exploited, and instead linear relationships between the phases are used (phase closures and differential phases). Consequently, specific...
We present a fast unsupervised myopic deconvolution method dedicated to quasi-real time processing of video sequences such as angiograms. Our method is based on a Bayesian approach of which the tuning parameters are automatically set thanks to the marginalized likelihood of the observed image. We demonstrate the effectiveness of our approach on simulated and empirical images.
This paper presents a 3D deconvolution method for fluorescence microscopy that reached the first place at the “the 3D Deconvolution Microscopy Challenge” held during ISBI 2013. It uses sparse coding algorithm to learn 2D “high resolution” features that will be used as a prior to enhance the resolution along depth axis. This is a three steps method: (i) deconvolution step with total variation regularization,...
In this paper we propose a blind deconvolution algorithm for wide field fluorescence microscopy. The 3D PSF is modeled after a parametrized pupil function. The PSF parameters are estimated jointly with the object in a maximum a posteriori framework. We illustrate the performances of our algorithm on experimental data and show significant resolution improvement notably along the depth. Quantitative...
In this paper we present a method for hyper-spectral image restoration for integral field spectrographs (IFS) data. It takes advantage of all the spectral and spatial correlations in the observed scene to enhance the spatial resolution. We illustrate this method with simulations coming from the Multi Unit Spectroscopic Explorer (MUSE) instrument. It shows the clear increase of the spatial resolution...
We present a new scheme for deconvolution of heterogeneous multidimensional data (e.g. spatio-temporal or spatio-spectral). It is derived, in a very general way, following an inverse problem approach. This method exploits the continuity of both object and PSF along the different dimensions to elaborate separable constraints. This improves the effectiveness and the robustness of the deconvolution technique...
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