where d(t) is the recorded signal and n(t) is the additive sensor noise signal. Some methods (Champagnat et al., 1996; Lavielle, 1993) used in Bayesian formulation the prior hypothesis that the reflectivity signal r(t) is a Bernouilli-Gaussian process. The first step is a detection of the reflectors and it follows by a magnitude estimation. The high noise level on recordings limits the performa...

In image acquisition, the captured image is often the result of the object being convolved with a blur function. Deconvolution is necessary in order to undo the effects of the blur. However, in real life we may have very little knowledge of the blur, and therefore we have to perform blind deconvolution. One major challenge of existing iterative algorithms for blind deconvolution is the enforcem...

In this paper, a novel algorithm for separating mixtures of multiple speech signals measured by multiple microphones in a room environment is proposed. The algorithm is a modification of an existing approach for density-based multichannel blind deconvolution using natural gradient adaptation. It employs linear predictors within the coefficient updates and produces separated speech signals whose...

This paper describes two efficient realizations of an adaptive multichannel blind deconvolution algorithm based on the natural gradient algorithm originally proposed by Amari, Douglas, Cichocki, and Yang. The proposed algorithms use fast convolution and correlation techniques and operate primarily in the frequency domain. Since the cost function minimized by the algorithms is welldefined in the...

In rocket telemetry imaging systems, the diffraction of the optics can generate a blurred effect and the onboard electronic devices can add noise. In order to solve these problems, this paper proposes a new image restoration scheme that joins Blind Deconvolution and Wavelet Packet. This process takes advantage of Blind Deconvolution for restoring the degraded image with partial or even no infor...

Rosen, Park, and Glick proposed the structured total least norm (STLN) algorithm for solving problems in which both the matrix and the right-hand side contain errors. We extend this algorithm for ill-posed problems by adding regularization, and we use the resulting algorithm to solve blind deconvolution problems as encountered in image deblurring when both the image and the blurring function ha...

1047-3203/$ see front matter 2011 Elsevier Inc. A doi:10.1016/j.jvcir.2011.07.010 ⇑ Corresponding author. E-mail addresses: [email protected] (J.-H. Lee Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since p...

This paper proposes a new approach to the seismic blind deconvolution problem in the case of band-limited seismic data characterized by low dominant frequency and short data records, based on Csiszár’s f-divergence. In order to model the probability density function of the deconvolved data, and obtain the closed form formula of Csiszár’s f-divergence, mixture Jones’ family of distributions (MJ)...

Many conventional blind image deconvolution algorithms have much processing time because the consideration of various cases and the optimization are required for general employment. Moreover, these algorithms are also complex to deal with various point spread functions (PSF). Therefore, this paper restricts the type of PSF to the dual-exposure type and proposes effective PSF search algorithm, u...

In this paper we study geometrical structures on the manifold of FIR lters and their application to multichannel blind deconvolution First we introduce the Lie group and Riemannian metric to the manifold of FIR lters Then we derive the natu ral gradient on the manifold using the isometry of the Riemannian metric Using the natural gradient we present a novel learning algorithm for blind deconvol...