In seismic data processing, deconvolution plays a very important role because it permits to increase the temporal resolution of seismic sections and to equalize sources. The deconvolution problem when the wavelet is known is an ill-posed problem that can be tackled via regularization methods. However, the seismic source wavelet is unknown and therefore, it must be estimated from the data prior ...

Introduction:. Standard spherical deconvolution approaches assume that HARDI signal can be modeled as a convolution between a common fiber response and a fiber orientation distribution function [1][2]. Recent spherical deconvolution methods improved the quality of the results introducing the non negative constraint of the solution [3][4][5]. It can be verified, however, that instability problem...

Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the proc...

A high-resolution retinal imaging camera is described that uses a Shack-Hartmann wave-front sensor and a Fourier deconvolution imaging technique. The operation of the camera is discussed in detail and high-resolution retinal images of the human cone mosaic are shown for a retinal patch approximately 10 arc min in diameter from two different retinal locations. The center-to-center cone spacing i...

Lateral crosstalk in CMOS imaging arrays deter effective utilization of small pixel sizes (e.g., 5 0 m 5 0 m) now permitted by technology scaling. A simple measurement setup for empirical characterization of lateral crosstalk in CMOS image sensors is presented. A demonstration of deblurring operations based on the obtained blur model of lateral crosstalk is also provided. Several well-known lin...

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...

Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusionCMR postprocessing is to recover tissue impulse-response from observed signalintensity curves. While several deconvolution techniques are available for this purpose, all of them use models with varying parameters for the representation of the impulse-re...

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.