Fuzzy retina fundus image enhancement method based on generative adversarial network

A fundus image and retinal technology, applied in image enhancement, biological neural network model, image analysis, etc., can solve the problems of fundus image enhancement, unfavorable ophthalmologist to accurately diagnose diseases, complex network model, etc.

Active Publication Date: 2019-09-20
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF3 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the following two defects in the existing retinal fundus image enhancement methods: 1) the obtained color fundus image loses important image features and color information, and while the contrast is improved, the local noise of the fundus image is also eliminated. Enhancement is not condu

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
  • Fuzzy retina fundus image enhancement method based on generative adversarial network
  • Fuzzy retina fundus image enhancement method based on generative adversarial network
  • Fuzzy retina fundus image enhancement method based on generative adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0087] Example 1

[0088] This embodiment illustrates the specific implementation of the method for enhancing the fuzzy retinal fundus image based on the generation countermeasure network of the present invention.

[0089] figure 1 It is a schematic diagram of the generative countermeasure network structure based on the fuzzy retinal fundus image enhancement method based on the generative countermeasure network of the present invention;

[0090] figure 1 Where G represents the generator, D represents the discriminator, the solid line represents the updated generator parameters, the dashed line represents the updated discriminator parameters; the input of the generator is the preoperative blurred fundus image, and the output is the generated enhanced image; the input of the discriminator is generated Enhanced image and clear fundus image after surgery; Loss_L1 updates generator parameters by calculating the L1 loss of the generated enhanced image and clear fundus image after surgery; ...

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 relates to a fuzzy retina fundus image enhancement method based on a generative adversarial network, and belongs to the technical field of image enhancement. The method comprises the following steps of 1, constructing a training set and a test set; 2, respectively preprocessing the fundus images in the training set and the test set constructed in the step 1; 3, constructing a generative adversarial network model for enhancement; and 4, enhancing the fuzzy retina fundus image of the test set by using the trained generator to obtain a final enhancement result. Compared with an existing fundus image enhancement method, the method has the advantages that the enhancement result is smooth and noiseless, the color restoration degree is high, and the problems of color offset, overhigh contrast degree, color distortion and noise amplification of a traditional method are effectively solved; a complex prior model is prevented from being designed, and the processing speed is higher; the details, such as blood vessel distribution of the retinal fundus image, etc., are well restored, and the generation result is real and reliable.

Description

technical field [0001] The invention relates to a fuzzy retinal fundus image enhancement method based on a generation confrontation network, and belongs to the technical field of image enhancement. Background technique [0002] Fundus images of the retina are used by ophthalmologists to diagnose a variety of retinal disorders. Due to defects in the imaging process or ocular lesions, some fundus images are of poor quality, mainly including blurred images, incorrect focus, uneven illumination, and low contrast. The obtained fundus images cannot meet the needs of clinical diagnosis, and are not suitable for computer-aided diagnosis of retinal diseases. Therefore, it is necessary to improve the anatomical structure visibility of these images so that the processed image quality is suitable for further clinical diagnosis and intelligent processing needs. [0003] Existing studies have proposed some grayscale or color retinal image enhancement methods, which can be roughly divide...

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
IPC IPC(8): G06T5/00G06T7/00G06T7/136G06T7/33G06N3/04G06N3/08
CPCG06T5/003G06T7/0012G06T7/33G06T7/136G06N3/084G06T2207/30041G06T2207/20036G06T2207/20081G06N3/045
Inventor 李慧琦杨邴予杨卫华赵赫
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap