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:
Aug. 05, 2025

Filed:

Dec. 18, 2024
Applicant:

Shandong Normal University, Shandong, CN;

Inventors:

Pu Huang, Shandong, CN;

Dengwang Li, Shandong, CN;

Jie Xue, Shandong, CN;

Yao Cheng, Shandong, CN;

Bin Jin, Shandong, CN;

Haitao Niu, Shandong, CN;

Guangyong Zhang, Shandong, CN;

Xiangyu Zhai, Shandong, CN;

Hao Li, Shandong, CN;

Baolong Tian, Shandong, CN;

Linchuan Nie, Shandong, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 1/00 (2006.01); A61B 1/313 (2006.01); G06T 5/20 (2006.01); G06T 5/50 (2006.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 5/92 (2024.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 11/00 (2006.01);
U.S. Cl.
CPC ...
A61B 1/000095 (2022.02); A61B 1/000096 (2022.02); A61B 1/3132 (2013.01); G06T 5/20 (2013.01); G06T 5/50 (2013.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 5/92 (2024.01); G06T 7/0014 (2013.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 11/00 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20182 (2013.01); G06T 2207/30004 (2013.01); G06T 2210/41 (2013.01);
Abstract

Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.


Find Patent Forward Citations

Loading…