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Sparsity Promoting Image Reconstruction

Investigators: Emrah Bostan, Ulugbek Kamilov

Introduction

Since the discovery of wavelet transforms, sparsity became a principal tool in signal and image reconstruction. Gradually, the Fourier-based linear methods are being replaced by powerful nonlinear alternatives. We consider the problem of image estimation from incomplete or inaccurate measurements, utilizing sparsity to get improved error performance.

Main Contributions

We developed efficient signal-estimation algorithms based on the newly developed theory of sparse stochastic processes. The algorithms are based on statistical interpretation of the data to achieve error performance superior to current state-of-the-art algorithms.

Collaborations: Michael Unser

Period: 2011-ongoing

Funding: ERC Advanced Researcher Grant

Major Publications

[1] 

M. Unser, P.D. Tafti, "Stochastic Models for Sparse and Piecewise-Smooth Signals," IEEE Transactions on Signal Processing, vol. 59, no. 3, pp. 989-1006, March 2011.

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