A Douglas-Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery
Under consideration is the large body of signal recovery problems that can be formulated as the problem of minimizing the sum of two (not necessarily smooth) lower semicontinuous convex functions in a real Hilbert space. This generic problem is analyzed and a decomposition method is proposed to solv...
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| Veröffentlicht in: | IEEE journal of selected topics in signal processing 2007-12, Vol.1 (4), p.564-574 |
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| container_title | IEEE journal of selected topics in signal processing |
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| creator | Combettes, P.L. Pesquet, J.-C. |
| description | Under consideration is the large body of signal recovery problems that can be formulated as the problem of minimizing the sum of two (not necessarily smooth) lower semicontinuous convex functions in a real Hilbert space. This generic problem is analyzed and a decomposition method is proposed to solve it. The convergence of the method, which is based on the Douglas-Rachford algorithm for monotone operator-splitting, is obtained under general conditions. Applications to non-Gaussian image denoising in a tight frame are also demonstrated. |
| doi_str_mv | 10.1109/JSTSP.2007.910264 |
| format | Article |
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| ispartof | IEEE journal of selected topics in signal processing, 2007-12, Vol.1 (4), p.564-574 |
| issn | 1932-4553 1941-0484 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Computation and Language Computer Science Convergence Convex optimization denoising Douglas-Rachford frame Hilbert space Image denoising Mathematical model Noise reduction nondifferentiable optimization Poisson noise Projection algorithms proximal algorithm Signal analysis Signal processing Signal processing algorithms wavelets |
| title | A Douglas-Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery |
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