| Paper: | IMDSP-P15.6 | ||
| Session: | Image Restoration and Denoising | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Image and Multidimensional Signal Processing: Restoration and Enhancement | ||
| Title: | Total Variation-Based Image Deconvolution: A Majorization-Minimization Approach | ||
| Authors: | José Bioucas-Dias, Mário Figueiredo, João Oliveira, Instituto Superior Técnico, Portugal | ||
| Abstract: | The total variation regularizer is well suited to piecewise smooth images. If we add the fact that these regularizers are convex, we have, perhaps, the reason for the resurgence of interest on TV-based approaches to inverse problems. This paper proposes a new TV-based algorithm for image deconvolution, under the assumptions of linear observations and additive white Gaussian noise. To compute the TV estimate, we propose a "majorization-minimization" approach, which consists in replacing a difficult optimization problem by a sequence of simpler ones, by relying on convexity arguments. The resulting algorithm has O(N) computational complexity, for finite support convolutional kernels. In a comparison with state-of-the-art methods, the proposed algorithm either outperforms or equals them, with similar computational complexity. | ||
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