Blind deconvolution of images using optimal sparse representations

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 ...

Optimal Dictionaries for Sparse Solutions of Multi-frame Blind Deconvolution

Abstract: Sparse representation of data has grown rapidly in signal processing. The benefits of sparse regularization are economy of representation of many different varieties of data, as well as control of difficult aspects of inverse problems, e.g., regularization of ill-conditioned inverse problems. Herein we represent atmospheric turbulence point-spread-functions by training optimal overcom...

Modified Sparse Multichannel Blind Deconvolution

Broadband sparse-array blind deconvolution using frequency-difference beamforming.

Synthetic time reversal (STR) is a technique for blind deconvolution of receiving-array recordings of sound from an unknown source in an unknown multipath environment. It relies on generic features of multipath sound propagation. In prior studies, the pivotal ingredient for STR, an estimate of the source-signal's phase (as a function of frequency ω), was generated from conventional beamforming ...

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 ...