Abstract. In many inverse problems it is essential to use regularization methods that preserve edges in the reconstructions, and many reconstruction models have been developed for this task, such as the Total Variation (TV) approach. The associated algorithms are complex and require a good knowledge of large-scale optimization algorithms, and they involve certain tolerances that the user must choose. We present a simpler approach that relies only on standard computational building blocks in matrix computations, such as orthogonal transformations, preconditioned iterative solvers, Kronecker products, and the discrete cosine transform — hence the term “plug-and-play. ” We do not attempt to improve on TV reconstructions, but rather provide an ...
Abstract—We introduce a novel family of invariant, convex, and non-quadratic functionals that we emp...
Abstract—We introduce a novel family of invariant, convex, and non-quadratic functionals that we emp...
Image reconstruction is a mathematically illposed problem and regularization methods are often used ...
We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order ex...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
The reconstruction of an image u(x, y) that describes a real scene from experimen-tal data (observed...
none3siImage restoration is an inverse problem that has been widely studied in recent years. The tot...
Abstract. Algorithms based on Total Variation (TV) minimization are prevalent in image processing. T...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
Image restoration is an inverse problem that has been widely studied in recent years. The total vari...
Image restoration is an inverse problem that has been widely studied in recent years. The total vari...
The Mumford–Shah model is a very powerful variational approach for edge preserving regularizat...
Abstract—We introduce a novel family of invariant, convex, and non-quadratic functionals that we emp...
Abstract—We introduce a novel family of invariant, convex, and non-quadratic functionals that we emp...
Image reconstruction is a mathematically illposed problem and regularization methods are often used ...
We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order ex...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
The reconstruction of an image u(x, y) that describes a real scene from experimen-tal data (observed...
none3siImage restoration is an inverse problem that has been widely studied in recent years. The tot...
Abstract. Algorithms based on Total Variation (TV) minimization are prevalent in image processing. T...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
Image restoration is an inverse problem that has been widely studied in recent years. The total vari...
Image restoration is an inverse problem that has been widely studied in recent years. The total vari...
The Mumford–Shah model is a very powerful variational approach for edge preserving regularizat...
Abstract—We introduce a novel family of invariant, convex, and non-quadratic functionals that we emp...
Abstract—We introduce a novel family of invariant, convex, and non-quadratic functionals that we emp...
Image reconstruction is a mathematically illposed problem and regularization methods are often used ...