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Conference Papers Year : 2011

Adaptive Structured Block Sparsity Via Dyadic Partitioning


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.
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hal-00597772 , version 1 (01-06-2011)


  • HAL Id : hal-00597772 , version 1


Gabriel Peyré, Jalal M. Fadili, Christophe Chesneau. Adaptive Structured Block Sparsity Via Dyadic Partitioning. EUSIPCO 2011, Aug 2011, Barcolona, Spain. pp.1455-1459. ⟨hal-00597772⟩
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