We report the use of a phase retrieval procedure based on maximum likelihood estimation (MLE) to produce an improved, experimentally calibrated model of a point spread function (PSF) for use in three-dimensional (3D) localization microscopy experiments. The method estimates a global pupil phase function (which includes both the PSF and system aberrations) over the full axial range from a simple...

A key aspect for virtual-reality based ultrasound training is the plausible simulation of the characteristic noise pattern known as ultrasonic speckle. The formation of ultrasonic speckle can be approximated efficiently by convolving the ultrasound point-spread function (PSF) with a distribution of point scatterers. Recent work extracts the latter directly from ultrasound images for use in forw...

We present an iterative approach to solve separable nonlinear least squares problems arising in the estimation of wavelength-dependent point spread function (PSF) parameters for hyperspectral imaging. A variable projection Gauss-Newton method is used to solve the nonlinear least squares problem. An analysis shows that the Jacobian can be potentially very ill-conditioned. To deal with this ill-c...

Context. The control of systematic effects when measuring background galaxy shapes is one of the main challenges for cosmic shear analyses. Aims. Study the fundamental limitations on shear accuracy due to the measurement of the point spread function (PSF) from the finite number of stars that are available. We translate the accuracy required for cosmological parameter estimation to the minimum n...

The lack of human performance data in human reliability analysis (HRA) has been lamented in the literature for a long time. It can increase the variability, uncertainty, unreliability of HRA outcomes to probabilistic risk analysis (PRA). This situation is even worse for PRA/HRA in digital main control rooms (MCRs) of nuclear power plants (NPPs). Expert judgment is used when data is unavailable....

This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred images to a same feature subspace by the method of multidimensional scaling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the ma...

A framework for fast multiview fusion of Single Plane Illumination Microscopy (SPIM) images based on a spatially-variant point spread function (PSF) model recorded using fluorescent point markers (beads) inserted into the surrounding medium is presented. The beads are used for the registration of the images as well as for the estimation of the spatially-varying PSF function. A new fusion algori...

We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm [1]. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical par...