Blind deconvolution is a common method for restoration of blurred text images, while binarization is employed to analyze and interpret the text semantics. In literature, these tasks are typically treated independently. This paper introduces a novel binarization driven blind deconvolution approach to couple both tasks in a common framework. The proposed method is derived as an energy minimizatio...

Nonstationary deconvolution has emerged as a tool for extracting the most realistic earth reflectivity from seismic traces, because of its adaptability to the characteristics of a given data set. Recently, deconvolution using the Gabor transform has been extended to yield deconvolution operators that are not only nonstationary in time, but also surface-consistent. We describe here a further ext...

We study the BV-α-Deconvolution model. It is a family of regularizations of the Barotropic Vorticity (BV) model that generalize the BV-α model and improve its accuracy. A both unconditionally stable and optimally convergent scheme for the BV-α-Deconvolution model is proposed and we show that it is O(α), where N is the deconvolution order, whereas the BV-α model is at most second order accurate....

Introduction: Hydronephrosis is one of the major causes of referring infants for nuclear medicine investigation. Renal dynamic study is the most reliable investigation for such children. However direct evaluation of renogram leads to generation of parameters that have no physiological interpretation. The configuration of renogram is very much dependent upon the rate at which radiopharmace...

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In this paper we present a new multichannel blind deconvolution method based on so-called subspace technique that was originally proposed by Harikumar and Bresler. When at least two differently degraded images (channels) of the original scene are provided, the method is better conditioned than classical single channel ones. In comparison with earlier multichannel blind deconvolution techniques ...

In this paper, we address the problem of the separation of convolutive mixtures even in the case where the non-Gaussian source signals are not linear processes. In this context, we show that the contrast functions, previously introduced in the case of one-input/one-output blind deconvolution and then in linear source sepertion, allow one to separate the sources by a de#ation approach. Some part...

Current drug discovery is dominated by label-dependent molecular approaches, which screen drugs in the context of a predefined and target-based hypothesis in vitro. Given that target-based discovery has not transformed the industry, phenotypic screen that identifies drugs based on a specific phenotype of cells, tissues, or animals has gained renewed interest. However, owing to the intrinsic com...

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 ...