The iterative blind deconvolution algorithm proposed by Ayers and Dainty [Opt. Lett. 13,547 (1988)] and improved on by Davey et al. [Opt. Commun. 69,353 (1989)] is applied to the problem of phase retrieval, which is a special case of the blind deconvolution problem. A close relationship between this algorithm and the error-reduction version of the iterative Fourier-transform phase-retrieval alg...

The aim of the present Letter is to introduce a new blind deconvolution algorithm based on fixed-point optimization of a ‘Bussgang’-type cost function. The cost function relies on approximate Bayesian estimation achieved by an adaptive neuron. The main feature of the presented algorithm is fast convergence that guarantees good deconvolution performances with limited computational demand compare...

Most of the techniques for image restoration are based on some known degradation models. But in many situations it is difficult to accurately measure the degradation factors or noise type that is the real motivation behind the use of blind deconvolution technique for image restoration. Here the observed degraded image is restored without having any prior knowledge about the noise type. Most of ...

We propose a new two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of speech, in which a new Single-Input Multiple-Output (SIMO)-modelbased ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at th...

We use blind deconvolution methods in optical diffusion tomography to reconstruct images of objects imbedded in or located behind turbid media from continuous-wave measurements of the scattered light transmitted through the media. In particular, we use a blind deconvolution imaging algorithm to determine both a deblurred image of the object and the depth of the object inside the turbid medium. ...

We propose a new two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of speech, in which a new Single-Input Multiple-Output (SIMO)-model-based ICA (SIMOICA) and blind multichannel inverse filtering are combined. SIMOICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the...

In this paper, we study convergence and e ciency of the batch estimator and natural gradient algorithm for blind deconvolution. First, the blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. To improve the learning e ciency of the online algorithm, explicit standardized estimating function...

This paper is a supplement and update to the reviews by Kundur and Hatzinakos [7,8] on blind image deconvolution. Most of the methods reviewed in [7,8] require that the PSF and the original image must be irreducible. However, this irreducibility assumption is not true in some important types of applications, such as when the PSF is Gaussian, which is a good model for many imaging systems. After...