**Variational methods for imaging**

One of the most difficult challenges in scientific computing is the development of algorithms and software for large scale ill-posed inverse problems, such as imaging denoising and deblurring. Such problems are extremely sensitive to perturbations (e.g. noise) in the data. To compute a physically reliable approximation from given noisy data, it is necessary to incorporate appropriate regularization into the mathematical model. Numerical methods to solve the regularized problem require effective numerical optimization strategies and efficient large scale matrix computations. In these lectures we describe first and second-order methods, dual or primal-dual approaches, and Bregman-type schemes and how to efficiently implement the ideas with iterative methods on realistic large scale imaging problems.

Docenti: Valeria Ruggiero - Luca Zanni

18-20 January 2016 (15 ore)

Lectures start Monday - January, 18, 2016, 9:00

Ferrara, Scientific-Technological Campus, Building B, Seminar room.

Materials

Splitting methods for monotone operators (V. Ruggiero)

Lagrangian methods for convex optimization problems

I. Methods

II. Applications in Imaging Lab

First-order methods for differentiable optimization in imaging

I . Scaled projection methods

II. Applications in Imaging

III. Lab

]]>**Variational methods for imaging**

One of the most difficult challenges in scientific computing is the development of algorithms and software for large scale ill-posed inverse problems, such as imaging denoising and deblurring. Such problems are extremely sensitive to perturbations (e.g. noise) in the data. To compute a physically reliable approximation from given noisy data, it is necessary to incorporate appropriate regularization into the mathematical model. Numerical methods to solve the regularized problem require effective numerical optimization strategies and efficient large scale matrix computations. In these lectures we describe first and second-order methods, dual or primal-dual approaches, and Bregman-type schemes and how to efficiently implement the ideas with iterative methods on realistic large scale imaging problems.

Docenti: Valeria Ruggiero - Luca Zanni

18-20 January 2016 (15 ore)

Lectures start Monday - January, 18, 2016, 9:00

Ferrara, Scientific-Technological Campus, Building B, Seminar room.

Materials

Splitting methods for monotone operators (V. Ruggiero)

Lagrangian methods for convex optimization problems

I. Methods

II. Applications in Imaging Lab

First-order methods for differentiable optimization in imaging

I . Scaled projection methods

II. Applications in Imaging

III. Lab

]]>**Variational methods for imaging**

One of the most difficult challenges in scientific computing is the development of algorithms and software for large scale ill-posed inverse problems, such as imaging denoising and deblurring. Such problems are extremely sensitive to perturbations (e.g. noise) in the data. To compute a physically reliable approximation from given noisy data, it is necessary to incorporate appropriate regularization into the mathematical model. Numerical methods to solve the regularized problem require effective numerical optimization strategies and efficient large scale matrix computations. In these lectures we describe first and second-order methods, dual or primal-dual approaches, and Bregman-type schemes and how to efficiently implement the ideas with iterative methods on realistic large scale imaging problems.

Docenti: Valeria Ruggiero - Luca Zanni

18-20 January 2016 (15 ore)

Lectures start Monday - January, 18, 2016, 9:00

Ferrara, Scientific-Technological Campus, Building B, Seminar room.

Materials

I. Methods

II. Applications in Imaging Lab

First-order methods for differentiable optimization in imaging

I . Scaled projection methods

II. Applications in Imaging

III. Lab

]]>

Prof. Valeria Ruggiero has been confirmed as director of the National Group of Scientific Computing (GNCS) of Indam

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