Structured Local Optima in Sparse Blind Deconvolution

Modified Sparse Multichannel Blind Deconvolution

Blind Deconvolution with Sparse Priors on the Deconvolution Filters

In performing blind deconvolution to remove reverberation from speech signal, most acoustic deconvolution filters need a great many number of taps, and acoustic environments are often time-varying. Therefore, deconvolution filter coefficients should find their desired values with limited data, but conventional methods need lots of data to converge the coefficients. In this paper, we use sparse ...

Wavelet Estimation via Sparse Multichannel Blind Deconvolution

Blind Deconvolution with Non-local Sparsity Reweighting

Blind deconvolution has made significant progress in the past decade. Most successful algorithms are classified either as Variational or Maximum a-Posteriori (MAP ). In spite of the superior theoretical justification of variational techniques, carefully constructed MAP algorithms have proven equally effective in practice. In this paper, we show that all successful MAP and variational algorithms...

Optimal Sparse Representations for Blind Deconvolution of Images

The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsification, which allows ...