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:
Apr. 21, 2026

Filed:

Sep. 29, 2025
Applicant:

Institute of Biophysics, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Dong Li, Beijing, CN;

Chang Qiao, Beijing, CN;

Xingye Chen, Beijing, CN;

Quan Meng, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G01N 21/64 (2006.01); G06T 3/20 (2006.01); G06T 3/4053 (2024.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G02B 21/16 (2006.01); G02B 21/36 (2006.01);
U.S. Cl.
CPC ...
G06T 3/4053 (2013.01); G01N 21/6428 (2013.01); G06T 3/20 (2013.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G01N 2021/6439 (2013.01); G02B 21/16 (2013.01); G02B 21/367 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01);
Abstract

A self-supervised multimodal structured illumination microscopic reconstruction method comprises: exciting a biological sample with structured illumination to obtain J raw fluorescence image sequences, wherein J is an integer greater than or equal to 1, each raw fluorescence image sequence comprises S fluorescence images, S being an integer greater than or equal to 2; generating a training set for each raw fluorescence image sequence among the J raw fluorescence image sequences; training a denoising neural network on the basis of the training set; and performing super-resolution reconstruction on the S fluorescence images in each of the J raw fluorescence image sequences using a standard structured illumination super-resolution reconstruction algorithm to form a super-resolution image, which is input into the denoising neural network to obtain a final super-resolution reconstructed image.


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