The multichannel exact blind image deconvolution theory tells us that exact recovery of unknown blur kernels is possible from multiple measurements of an identical scene through distinct blur channels. However, in many biological applications, there often exist technical difficulties in obtaining multiple distinct blur measurements, since the image content may vary for various reasons, includin...

We carry out a classification of the observed pulsar dataset into distinct clusters, based on $P-\dot{P}$ diagram, using Extreme Deconvolution Gaussian Mixture Model. then use Bayesian Information Criterion to select optimum number clusters. find in accord with previous works, that can be optimally classified six two for millisecond population, and four ordinary population. Beyond that, however...

Passive multi-frequency microwave sensors are indispensable instruments for worldwide environmental monitoring. However, they often suffer from the issues of poor spatial resolution and original land–sea transition zone data contaminated severely. Conventional analytical deconvolution methods enhance at expense noise amplification Gibbs fluctuations in zone. In order to as well simultaneously i...

■ CONTENTS Streamlining Data Acquisition: The First Step B Data Alignment B Automated Metabolomics B Data Streaming For Cloud-Based Metabolomics C Feature Analysis C Mass Spectral Annotations C Credentialing D Calculating Mass Measurement Errors D Statistical Analysis: Visualization and Deconvolution E Targeted Validation F Pathway Analysis: Putting Metabolomic Data into a Biological Context F ...

Deconvolution is an indispensable tool in image processing and computer vision. It commonly employs fast Fourier transform (FFT) to simplify computation. This operator, however, needs to transform from and to the frequency domain and loses spatial information when processing irregular regions. We propose an efficient spatial deconvolution method that can incorporate sparse priors to suppress no...

We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram mask...

conclusion the myocardial grayscale intensity, velocity and displacement can effectively reflect the situation of steiri in patients after cardiac operation, and ti (cvi) has a higher performance in discriminating between patients with and without steiri. background the timely diagnosis of presence or absence of reperfusion injury after cardiac operation is critical for the patient’s outcome. w...

We show that automatic determination of regularization threshold and pre-filtering of 3-D fluorescence microscopic images improves the stability of deconvolution results when using the Linear Least squares Solution or the Maximum a Posteriori method. Doing so, the choice of the regularization parameter much less depends on a priori knowledge of the specimen or skills of the operator. This incre...

This Article is brought to you for free and open access by the Center for Coastal Physical Oceanography at ODU Digital Commons. It has been accepted for inclusion in CCPO Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. Repository Citation Pruett, C. D.; Thomas, B. C.; Grosch, C. E.; and Gatski, T. B., "A Temporal A...

Background First pass perfusion CMR is a well-validated technique for the detection of inducible myocardial ischaemia. However, limited data are available on the feasibility of first pass perfusion CMR and quantitative deconvolution analysis in the context of reduced LV function. The aim of this study was to assess effects of reduced LV function and LV dilatation on the characteristics of signa...