Jérôme Darbon

Bregman Iterative Algorithms for

We propose simple and extremely efficient methods for solving the basis pursuit problem

\ell_1

\min\{\|u\|_1 : Au = f, u\in\mathbb{R}^n\},

-Minimization with Applications to Compressed Sensing

which is used in compressed sensing. Our methods are based on Bregman iterative regularization, and they give a very accurate solution after solving only a very small number of instances of the unconstrained problem

Shape-based hand recognition

\min_{u\in\mathbb{R}^n} \mu\|u\|_1+\frac{1}{2}\|Au-f^k\|_2^2

Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization

for given matrix

Fast nonlocal filtering applied to electron cryomicroscopy

A

SAR Image Regularization With Fast Approximate Discrete Minimization

and vector

Image Restoration with Discrete Constrained Total Variation Part II: Levelable Functions, Convex Priors and Non-Convex Cases

f^k

Color image segmentation based on automatic morphological clustering

. We show analytically that this iterative approach yields exact solutions in a finite number of steps and present numerical results that demonstrate that as few as two to six iterations are sufficient in most cases. Our approach is especially useful for many compressed sensing applications where matrix-vector operations involving

Enhancement of historical printed document images by combining Total Variation regularization and Non-local Means filtering

A