We propose a two component method for denoising multidimensional signals, e.g. images. The first component uses a dynamic programing algorithm of complexity O(N log N) to find an ...
We present a practical, stratified autocalibration algorithm with theoretical guarantees of global optimality. Given a projective reconstruction, the first stage of the algorithm ...
In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity ...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
We consider the sparse Fourier transform problem: given a complex vector x of length n, and a parameter k, estimate the k largest (in magnitude) coefficients of the Fourier transf...
Haitham Hassanieh, Piotr Indyk, Dina Katabi, Eric ...
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...