Name
Abstract | ||
|---|---|---|
This paper proposes a novel method to adapt the block-sparsity structure to the observed noisy data. Towards this goal, the Stein risk estimator framework is exploited, and the block-sparsity is dyadically organized in a tree. The adaptation of the sparsity structure is obtained by finding the best recursive dyadic partition, whose terminal nodes (leaves) are the blocks, that minimizes a data-driven estimator of the risk. Our main contributions are (i) analytical expression of the risk; (ii) a novel estimator of the risk; (iii) a fast algorithm that yields the best partition. Numerical results on wavelet-domain denoising of synthetic and natural images illustrate the improvement brought by our adaptive approach. |
Year | Venue | Keywords |
|---|---|---|
2011 | Barcelona | image denoising,wavelet transforms,stein risk estimator framework,adaptive structured block sparsity,best recursive dyadic partition,wavelet-domain denoising,estimation,noise reduction,noise measurement |
Field | DocType | ISSN |
Noise reduction,Noisy data,Mathematical optimization,Noise measurement,Algorithm,Partition (number theory),Mathematics,Recursion,Estimator | Conference | 2076-1465 |
Citations | PageRank | References |
4 | 0.50 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Peyré | 1 | 1195 | 79.60 |
| Jalal Fadili | 2 | 1184 | 80.08 |
| Christophe Chesneau | 3 | 6 | 2.26 |