We present an approach to determine sufficient conditions for the global convergence of iterative blind deconvolution algorithms using finite impulse response (FIR) deconvolution filters. The novel technique, which incorporates Lyapunov’s direct method, is general, flexible, and can be easily adapted to analyze the behavior of many types of nonlinear iterative signal processing algorithms. Spec...

The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smooth l1/l2 regularization term. As the mean of the noise in the power spectrum domain is dependent on its variance in the time domain, the proposed method includes a variance estimation step, which allows more ...

The deconvolution principle was used to evaluate the extent of absorption and first-pass elimination of selected drugs. In the first example, deconvolution of the portal blood profiles of etretinate (ET, a synthetic retinoid) indicated that there was significant gut-wall conversion of ET to acitretin (ETA, the primary metabolite of ET) during a 60-min intestinal perfusion of ET. In the second e...

objective(s): erythropoietin (epo), is a 34kda glycoprotein hormone, which belongs to type 1 cytokine superfamily. epo involves in erythrocyte maturation through inhibition of apoptosis in erythroid cells. besides its main function, protective effects of epo in heart and brain tissues have been reported. epo has a critical role in development, growth, and homeostasis of brain. furthermore epo h...

Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper characterizes the ambiguity space of blind deconvolution and shows unidentifia...

We review some of the important results on deconvolution, particularly the multichannel deconvolution problem, for the setting of Euclidean space, focusing on the central role of the Hörmander strongly coprime condition in this area of analysis. We then address the problem of deconvolution in the Heisenberg group setting, beginning with the results of [16]. We also extend the results of [16] to...

Convolution and Deconvolution is having wide area of application in Digital Signal Processing. Convolution helps to estimate the output of a system with arbitrary input, with knowledge of impulse response of the system. Linear systems characteristics are completely specified by the systems impulse response, as governed by the mathematics of convolution. And with the knowledge of impulse respons...

Image deconvolution is a challenging ill-posed problem when only partial information of the blur kernel is available. Certain regularization on sharp images has to be imposed to constrain the estimation of true images during the blind deconvolution process. Based on the observation that an image of sharp edges tends to minimize the ratio between the `1 norm and the `2 norm of its wavelet frame ...

This paper addresses the blind deconvolution of multi-input– multi-output (MIMO) FIR systems driven by white non-Gaussian source signals. First, we present a weaker condition on source signals than the so-called i.i.d. condition so that blind deconvolution is possible. Then, under this condition, we provide a necessary and sufficient condition for blind deconvolution of MIMO FIR systems. Finall...