|Title:||Sparsity Models for Signals: Theory and Applications
|Abstract:||Many signal and image processing applications have benefited remarkably from the theory of sparse representations.
In its classical form this theory models
signal as having a sparse representation under a given dictionary -- this is referred to as the "Synthesis Model".
this work we focus on greedy methods for the problem of recovering a signal
from a set of deteriorated linear measurements. We consider four different
sparsity frameworks that extend the aforementioned synthesis model: (i) The cosparse analysis model; (ii) the signal
space paradigm; (iii) the transform domain strategy; and (iv) the sparse Poisson noise model.
Our algorithms of interest in the first part of the work are the greedy-like schemes: CoSaMP, subspace pursuit (SP), iterative hard thresholding (IHT) and hard thresholding pursuit (HTP). It has been shown for the synthesis model that these can achieve a stable recovery under some RIP (restricted isometry property) based assumptions in the case that the noise is additive and adversarial. In this work we extend these results in several important ways:
(a) For the case that the noise is random white Gaussian we show that CoSaMP, SP and IHT achieve near-oracle performance, closing a gap between greedy algorithms and relaxation based techniques.
(b) We propose analysis variants for the greedy-like techniques. Assuming the availability of a near optimal projection scheme for the cosparse model, we provide performance guarantees for these algorithms. Our theoretical study relies on a RIP adapted to the context of the cosparse analysis model.
(c) We consider the recovery performance in the synthesis framework when the signal, rather than the representation, is the objective. We develop new uniqueness and stability conditions. We propose a variant of orthogonal matching pursuit (OMP) and give reconstruction guarantees for it using a generalized coherence definition. Then we study the recently proposed signal space CoSaMP (SSCoSaMP) and provide recovery guarantees that hold in several settings including those when the dictionary is incoherent or structurally coherent. These results align more closely with traditional results for representation recovery and improve upon previous work in the signal space setting.
(d) One drawback of the results for the analysis greedy-like algorithms is that they do not hold for frames as the analysis dictionary, while the existing guarantees for relaxation based methods do cover frames. We propose a variant of IHT that operates in the analysis transform domain and provide guarantees that close this gap.
In the last part of our work we look at the Poisson denoising problem. We propose to harness sparse-representation modeling of image patches for this denoising task, handling severe SNR scenarios. We employ an exponential sparsity model, as recently proposed by Salmon et al., relying directly on the true noise statistics. Our scheme uses a greedy pursuit, with boot-strapping based stopping criterion, and dictionary learning within the denoising process, leading to state-of-the-art-results.
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