Abstract We present a noise deconvolution technique to remove wide class of noises when performing arbitrary measurements on qubit systems. In particular, we derive the inverse map most common single noisy channels, and exploit it at data processing step obtain noise-free estimates observables evaluated system subject known noise. illustrate self-consistency check ensure that characterization i...

In spite of the huge literature on deconvolution problems, very little is done for hybrid contexts where signals are quantized. In this paper we undertake an information theoretic approach to the deconvolution problem of a simple integrator with quantized binary input and sampled noisy output. We recast it into a decoding problem and we propose and analyze (theoretically and numerically) some l...

In this paper we discuss the semi parametric statistical model for blind deconvolution. First we introduce a Lie Group to the manifold of noncausal FIR filters. Then blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. A natural gradient learning algorithm is developed for training noncausa...

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...

Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing i...

Introduction Several deconvolution methods have been proposed to increase the angular resolution of HARDI or QBI [1][2]. However, there is no deconvolution method directly applied to diffusion ODF without resorting to spherical decomposition. In this study, we developed a deconvolution method that could be directly applied to diffusion ODF, thus extending its applicability to other q-space meth...

This paper studies the inverse of min-plus convolution, i.e., min-plus deconvolution, in the set of non-negative, wide-sense increasing and causal functions. A sufficient condition for min-plus deconvolution to be closed in this set of functions is presented. Possible application of min-plus deconvolution to the service curve design has been discussed. Key-Words: Min-plus convolution, inverse p...

Blind deconvolution problems arise in many image restoration applications. Most available blind deconvolution methods are iterative. Recently, Justen and Ramlau proposed a novel non-iterative blind deconvolution method. The method was derived under the assumption of periodic boundary conditions. These boundary conditions may introduce oscillatory artifacts into the computed restoration. We desc...