Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Full-slice digital imaging two-step focusing restoration method based on deep learning

A technology of digital imaging and deep learning, applied in image enhancement, image analysis, image data processing, etc., to achieve the effect of saving the cost of instrument experiments

Inactive Publication Date: 2020-12-04
HARBIN INST OF TECH
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Considering the limitations of traditional methods, the present invention uses advanced machine learning algorithms to solve the quasi-focus restoration problem of full-slice digital imaging

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Full-slice digital imaging two-step focusing restoration method based on deep learning
  • Full-slice digital imaging two-step focusing restoration method based on deep learning
  • Full-slice digital imaging two-step focusing restoration method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] The specific implementation of the method of the present invention will be further described below.

[0030] to combine Figure 1 to Figure 3 As shown, a deep learning-based full-slice digital imaging two-step quasi-focus restoration method disclosed in this embodiment includes the following steps:

[0031] Step a, input out-of-focus image;

[0032] Step b, automatically focusing on the network;

[0033] Step c, predicting the quasi-focus distance;

[0034] Step d, distance compensation half quasi-focus image;

[0035] Step e, quasi-focus restoration network;

[0036] Step f, quasi-focus image,

[0037] The neural network method is used to realize the two-step quasi-focus restoration function of the whole slice digital imaging.

[0038] Specifically, the input defocused images come from z-stack image stacks obtained by axial scanning movement of different sub-image lateral positions, and each sub-image position obtains 20 positive and negative defocused images and...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention is based on the field of biomedical instruments, and particularly relates to a full-slice digital imaging two-step quasi-focus restoration method based on deep learning. The method provides a learning method based on a secondary compensation network, and mainly comprises an automatic focusing network and a quasi-focus restoration network. The designed refocusing network can calculatethe focusing distance corresponding to the out-of-focus image; moving the microscope workbench, performing motion compensation on the focusing distance, and performing secondary photographing to obtain a semi-focusing image; through dual-channel joint end-to-end network design of a focusing image and a semi-focusing image, the method realizes high-precision out-of-focus image restoration of full-slice digital imaging, and performs software virtualization on traditional full-slice digital pathological imaging hardware.

Description

technical field [0001] The present invention is based on the field of biomedical instruments, takes deep learning technology as the core, and specifically relates to a full-slice digital imaging two-step quasi-focus restoration method based on deep learning, which can be widely used in the fields of instrument science, artificial intelligence, medical imaging and automation, etc. Research. Background technique [0002] In recent years, advanced digital pathology imaging technology has been widely researched and applied. Whole Slide Images (WSI, Whole Slide Images), that is, virtual microscopy, can collect traditional microscopic sections in the form of digital images, which can achieve arbitrary computer access, easy storage, and communication between researchers and doctors. Off-site transmission, etc. Whole slide digital imaging is crucial in bioimaging research, such as in areas such as cancer analysis and disease prediction. At present, the US Food and Drug Administra...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06F30/27
CPCG06F30/27G06T2207/20081G06T2207/20084G06T2207/10056G06N3/045G06T5/00
Inventor 刘贤明李强
Owner HARBIN INST OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com