Detection of Flat Bottom Holes Using Sparse Deconvolution

Sparse deconvolution using adaptive mixed-Gaussian models

In this paper we present a new algorithm to recover a sparse signal from a noisy register. The algorithm assumes a new prior distribution for the sparse signal that consists of a mixture of a narrow and a broad Gaussian both with zero mean. A penalty term which favors solutions driven from this model is added to the usual error cost function and the resultant global cost function is minimized b...

Sparse Deconvolution Using Support Vector Machines

Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing i...

Deconvolution of sparse positive spikes

Deconvolution is usually regarded as one of the ill-posed problems in applied mathematics if no constraints on the unknowns are assumed. In this paper, we discuss the idea of welldefined statistical models being a counterpart of the notion of well-posedness. We show that constraints on the unknowns such as positivity and sparsity can go a long way towards overcoming the ill-posedness in deconvo...

Unsupervised deconvolution of sparse spike trains using stochastic approximation

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computati...