Leica Microsystems Cms Gmbh

Florian Ziesche

Kai Walter

Juergen Reymann

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

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

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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Mannheim, Germany

Florian Ziesche