نتایج جستجو برای: deconvolution analysis
تعداد نتایج: 2829005 فیلتر نتایج به سال:
The ‘Bussgang’ algorithm is one among the most known blind deconvolution techniques in the adaptive signal processing literature. It relies on a Bayesian estimator of the source signal that requires the prior knowledge of the source statistics as well as the deconvolution noise characteristics. In this paper, we propose to implement the estimator with a simple adaptive activation function neuro...
Biomedical images are often degraded during image acquisition. Two very common degradations that occur in many biomedical imaging modalities are blur and noise. Corrupted images are more difficult to interpret by the physician or researcher and so the goal is to restore the original image. Blurring can be mathematically modeled by convolution, and within this model the blur is characterized by ...
We present a Semi-Blind method for image deconvolution. This method uses a pre-processed image (via the shock filter) as an initial condition for total variation (TV) minimizing blind deconvolution. Using shock filter gives good information on location of the edges, and using variational functional such as Chan and Wong [T.F. Chan and C.K. Wong, Total variation blind deconvolution, IEEE Trans I...
A blind deconvolution algorithm based on the Richardson–Lucy deconvolution algorithm is presented. Its performance in the presence of noise is found to be superior to that of other blind deconvolution algorithms. Results are presented and compared with results obtained from implementation of a Weiner filter blind deconvolution algorithm. The algorithm is developed further to incorporate functio...
Methods of three-dimensional deconvolution with a point-spread function as frequently employed in optical microscopy to reconstruct true three-dimensional distribution of objects are extended to holographic reconstructions. Two such schemes have been developed and are discussed: an instant deconvolution using the Wiener filter as well as an iterative deconvolution routine. The instant 3d-deconv...
Application of regularized Richardson–Lucy algorithm for deconvolution of confocal microscopy images
Although confocal microscopes have considerably smaller contribution of out-of-focus light than widefield microscopes, the confocal images can still be enhanced mathematically if the optical and data acquisition effects are accounted for. For that, several deconvolution algorithms have been proposed. As a practical solution, maximum-likelihood algorithms with regularization have been used. Howe...
We derive the asymptotic distribution of the supremum distance of the deconvolution kernel density estimator to its expectation for certain supersmooth deconvolution problems. It turns out that the asymptotics are essentially different from the corresponding results for ordinary smooth deconvolution.
Introduction: Prior studies comparing Hemodynamic Response Function (HRF) in the young and elderly adults based on fMRI data have reported inconsistent findings for brain vision and motor regions in healthy aging. It is shown that the averaging method employed in all previous works has caused this inconsistency. The averaging is so sensitive to outliers and noise. However, fMRI data are o...
Recent advances in application fields such as blind deconvolution and independent component analysis have revived the interest in higher-order statistics (HOS), showing them to be an indispensable tool for providing piercing solutions to these problems. Multilinear algebra and in particular tensor analysis provide a natural mathematical framework for studying HOS in view of the inherent multiin...
The extended Euler deconvolution algorithm is shown to be a generalization and unification of 2-D Euler deconvolution and Werner deconvolution. After recasting the extended Euler algorithm in a way that suggests a natural generalization to three dimensions, we show that the 3-D extension can be realized using generalized Hilbert transforms. The resulting algorithm is both a generalization of ex...
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