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arXiv · 1103.2208

Linear inverse problems with noise: primal and primal-dual splitting

François-Xavier Dupé·Jalal Fadili·Jean-Luc Starck
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Abstract

In this paper, we propose two algorithms for solving linear inverse problems when the observations are corrupted by noise. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson). On the other hand, as a prior, the images to restore are assumed to be positive and sparsely represented in a dictionary of waveforms. Piecing together the data fidelity and the prior terms, the solution to the inverse problem is cast as the minimization of a non-smooth convex functional. We establish the well-posedness of the optimization problem, characterize the corresponding minimizers, and solve it by means of primal and primal-dual proximal splitting algorithms originating from the field of non-smooth convex optimization theory. Experimental results on deconvolution, inpainting and denoising with some comparison to prior methods are also reported.

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François-Xavier Dupé, Jalal Fadili, Jean-Luc Starck. 2011-03-11. Linear inverse problems with noise: primal and primal-dual splitting. https://arxiv.org/abs/1103.2208

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