Leica Microsystems Cms Gmbh

Kai Walter

Joachim Bradl

Constantin Kappel

Florian Ziesche

Benjamin Deissler

Lars Friedrich

Soeren Alsheimer

Juergen Reymann

Jürgen Reymann

A data processing apparatus for processing a digital input image is configured to receive the digital input image. The digital input image includes input photon arrival-time data at input image locations. The data processing apparatus is further configured to compute a digital output image based on the digital input image by deconvolution. The digital output image includes output photon arrival-time data at output image locations. The output photon arrival-time data represent an estimate of an unblurred ground-truth of the input photon arrival-time data. The data processing apparatus is further configured to compute the deconvolution by an iterative algorithm using an update function. The update function depends on a point-spread function, a previous estimate of the ground-truth, and the input photon arrival-time data.

Data processing apparatus and method for processing a digital image having photon arrival-time data

A signal processing apparatus for enhancing a digital input signal (I(x)) recorded by a recording system having a system response (H(x)), is configured to retrieve the digital input signal (I(x)) and compute a baseline estimate (ƒ(x)) of the digital input signal, the baseline estimate representing a baseline of the digital input signal and comprising features of the digital input signal that are larger than a feature length (fl). The apparatus is further configured to remove the baseline estimate from the digital input signal to obtain an output signal comprising spatial features that are smaller than the feature length, retrieve a characteristic length (cl) of the system response (H(x)) and compute the baseline estimate (ƒ(x)) using a feature length that is smaller than the characteristic length of the system response (H(x)).

Signal processing apparatus and method for enhancing a digital input signal

An apparatus for enhancing an input phase distribution (I(x)) is configured to retrieve the input phase distribution (I(x)) and compute a baseline estimate (ƒ(x)) as an estimate of a baseline (I(x)) in the input phase distribution (I(x)). The apparatus is further configured to obtain an output phase distribution (O(x)) based on the baseline estimate (ƒ(x)) and the input phase distribution (I(x)).

Image processing apparatus and method for enhancing a phase distribution

A method for estimating baseline in a signal, the signal being represented by input signal data (I(x)), includes estimating a baseline contribution (I(x)) in the signal to obtain baseline estimation data (f(x)), wherein the baseline estimation data (f(x)) are computed as a fit to at least a subset of the input signal data (I(x)) by minimizing a least-square minimization criterion (M(f(x))). Deblurred output signal data (O(x)) are obtained based on the baseline estimation data (f(x)) and the input signal data (I(x)). The least-square minimization criterion (M(f(x))) comprises a penalty term (P(f(x))).

Apparatus and method using baseline estimation and half-quadratic minimization for the deblurring of signal data

A method for estimating a stimulated emission depletion microscopy (STED) resolution includes generating a first frame representing a reference image from a field-of-view, the reference image having a predetermined reference resolution, and generating at least one second frame representing a STED image from the same field-of-view, the STED image having the STED resolution to be estimated. The at least one second frame is blurred by applying a convolution kernel with at least one fit parameter to the second frame. An optimal value of the at least one fit parameter of the convolution kernel is determined for which a difference between the first frame and the blurred at least one second frame is minimized. The STED resolution is estimated based on the optimal value of the at least one fit parameter and the predetermined reference resolution.

Method and device for estimating a STED resolution

An apparatus for baseline estimation in input signal data is configured to retrieve input signal data (I(x)) and to subtract baseline estimation data (&#x192;(x)) from the input signal data (I(x)) to compute output signal data. The apparatus is further configured to compute the baseline estimation data (&#x192;(x)) from a convolution using a discrete Green's function (G(x)).

Apparatus and method for baseline estimation in input signal data

An image processing device for improving signal-to-noise of microscopy images includes a memory configured to store microscopy images at least temporarily, and processing circuitry configured to determine an output image based on a weighted rolling average of an ordered set of microscopy images stored in the memory. A method for improving signal-to-noise of microscopy images includes storing microscopy images at least temporarily, and determining an output image based on a weighted rolling average of an ordered set of the stored microscopy images.

Image processing device, system, and method for improving signal-to-noise of microscopy images

A method for a deconvolution of a digital input image (I(x)) having a plurality of input voxels (x), in particular a digital input image obtained from a medical observation device, such as a microscope or endoscope and/or using fluorescence, includes computing a local signal-to-noise ratio (SNR(x)) within an input region (R(x)) of the digital input image, the input region consisting of a subset of the plurality of input voxels of the digital input image and surrounding the current input voxel. A noise component (&#x3b2;(SNR)) is computed from the local signal-to-noise ratio, the noise component representing image noise (([h*f](x), n(x)) in the deconvolution. The noise component is limited to a predetermined minimum noise value (&#x3b2;) for a local signal-to-noise ratio above a predetermined upper SNR threshold value (SNR) and is limited to a predetermined maximum noise value (&#x3b2;) for a local signal-to-noise ratio below a predetermined lower SNR threshold value (SNR).

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Schriesheim, Germany

Kai Walter