The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 23, 2025

Filed:

May. 31, 2022
Applicant:

Carl Zeiss Microscopy Gmbh, Jena, DE;

Inventors:

Manuel Amthor, Jena, DE;

Daniel Haase, Zoellnitz, DE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/045 (2023.01); G06F 17/11 (2006.01); G06N 3/04 (2023.01); G06T 3/4046 (2024.01); G06T 5/20 (2006.01); G06T 5/60 (2024.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06N 3/045 (2023.01); G06F 17/11 (2013.01); G06N 3/04 (2013.01); G06T 3/4046 (2013.01); G06T 5/20 (2013.01); G06T 5/60 (2024.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A method, device, and computer program product are designed for non-convolutional image processing in microscopy of an input image into an output image using an artificial neural network with at least one contracting path including layers, at least one expanding path including layers, and at least one filter kernel. The method includes determining, in one or multiple artificial neural network layers, a similarity metric between at least one filter kernel and one output of the previous layer. Additionally, in at least one layer of the contracting path, the resolution of the output of the previous layer is reduced, and, in at least one layer of the expanding path, the resolution of the output of the previous layer is increased. The first artificial neural network layer treats the input image as the output of the previous layer, and the output of the last artificial neural network layer is the output image.


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