Publications associées à Compute-and-Forward using nested linear codes for the Gaussian MAC

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Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan

We study the statistics of wavelet coefficients of non-Gaussian images, focusing mainly on the behaviour at coarse scales. We assume that an image can be whitened by a fractional Laplacian operator, which is consistent with an

∥ω∥ ^{ -\gamma }

spectral ...

IEEE2014

Compute-and-Forward using nested linear codes for the Gaussian MAC

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Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Benchmarking of Objective Quality Metrics for Colorless Point Clouds

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Linear Function Computation in Networks: Duality and Constant Gap Results

Computation over Gaussian networks with orthogonal components

A coordinate system for Gaussian Networks

Optimally Localized Wavelets and Smoothing Kernels

Stochastic Models for Sparse and Piecewise-Smooth Signals

Coding Along Hermite Polynomials for Gaussian Noise Channels

Computation over Gaussian networks with orthogonal components

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

Linear Function Computation in Networks: Duality and Constant Gap Results

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Optimally Localized Wavelets and Smoothing Kernels

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

A coordinate system for Gaussian Networks

Coding Along Hermite Polynomials for Gaussian Noise Channels

Benchmarking of Objective Quality Metrics for Colorless Point Clouds

Stochastic Models for Sparse and Piecewise-Smooth Signals