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Image Deconvolution (image + deconvolution)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Parallel deconvolution of large 3D images obtained by confocal laser scanning microscopy

MICROSCOPY RESEARCH AND TECHNIQUE, Issue 3 2010
Piotr Pawliczek
Abstract Various deconvolution algorithms are often used for restoration of digital images. Image deconvolution is especially needed for the correction of three-dimensional images obtained by confocal laser scanning microscopy. Such images suffer from distortions, particularly in the Z dimension. As a result, reliable automatic segmentation of these images may be difficult or even impossible. Effective deconvolution algorithms are memory-intensive and time-consuming. In this work, we propose a parallel version of the well-known Richardson,Lucy deconvolution algorithm developed for a system with distributed memory and implemented with the use of Message Passing Interface (MPI). It enables significantly more rapid deconvolution of two-dimensional and three-dimensional images by efficiently splitting the computation across multiple computers. The implementation of this algorithm can be used on professional clusters provided by computing centers as well as on simple networks of ordinary PC machines. Microsc. Res. Tech., 2010. © 2009 Wiley-Liss, Inc. [source]

Convergence analysis of blind image deconvolution via dispersion minimization

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7 2006
C. Vural
Abstract A new non-linear adaptive filter called blind image deconvolution via dispersion minimization has recently been proposed for restoring noisy blurred images blindly. This is essentially a two-dimensional version of the constant modulus algorithm that is well known in the field of blind equalization. The two-dimensional extension has been shown capable of reconstructing noisy blurred images using partial a priori information about the true image and the point spread function in a variety of situations by means of simulations. This paper analyses the behaviour of the algorithm by investigating the static properties of the cost function and the dynamic convergence of the parameter estimates. The theoretical results are supported with computer simulations. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Iterative ultrasonic signal and image deconvolution for estimation of the complex medium response

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 6 2005
Zhiping Mu
Abstract The ill-conditioned inverse problem of estimating ultrasonic medium responses by deconvolution of RF signals is investigated. The primary difference between the proposed method and others is that the medium response function is assumed to be complex-valued rather than restricted to being real-valued. Derived from the complex medium model, complex Wiener filtering is presented, and a Hilbert transform related limitation to inverse filtering type methods is discussed. We introduce a nonparametric iterative algorithm, the least squares method with point count regularization (LSPC). The algorithm is successfully applied to simulated and experimental data and demonstrates the capability of recovering both the real and imaginary parts of the medium response. The simulation results indicate that the LSPC method can outperform Wiener filters and improve the resolution of the ultrasound system by factors as high as 3.7. Experimental results using a single element transducer and a conventional medical ultrasound system with a linear array transducer show that despite the errors in pulse estimation and the noise in the RF signals, excellent results can be obtained, demonstrating the stability and robustness of the algorithm. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 266,277, 2005 [source]