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

Image denoising method and system

An image and image input technology, applied in the field of image processing, can solve the problems of long model training time, large memory consumption of deep convolutional neural networks, and difficulty in image denoising efficiency, so as to reduce parameters, improve effects, and reduce training parameters. Effect

Pending Publication Date: 2022-05-24
SHANDONG NORMAL UNIV
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the memory consumption of the deep convolutional neural network is still very large, and the training time of the model is relatively long, so it is difficult to have a high image denoising efficiency

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
  • Image denoising method and system
  • Image denoising method and system
  • Image denoising method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] This embodiment provides an image denoising method, such as image 3 shown, including the following steps:

[0042] Step 1. Obtain an image data set, add noise, and obtain an image training set.

[0043] Specifically, additive white Gaussian noise is selected as the noise type for training, and noise is added to the clean images of the image dataset, and the training set is composed of the original image and the noisy image.

[0044] Step 2: Use the image training set to train the denoising model, and use the original image in the training set and the training sample pair of the noisy image as the input of the network structure. like figure 1 As shown, the network structure adopts the multi-scale feature extraction block (multi-scale feature extraction module) and the Ghost feature extraction block (Ghost feature extraction module) in turn. After performing multi-feature extraction and Ghost-based feature extraction on the noisy image, the extracted Noise features, t...

Embodiment 2

[0064] This embodiment provides an image denoising system, which specifically includes the following modules:

[0065] an image acquisition module configured to: acquire a noise image;

[0066] a denoising module, which is configured to: input the noise image into the denoising model to obtain the denoising image;

[0067] Among them, the denoising model passes through the multi-scale feature extraction block and the Ghost feature extraction block in turn, and after the feature extraction of the noise image, the noise feature is obtained, and the noise feature is subtracted from the noise image to obtain the denoised image.

[0068] It should be noted here that each module in this embodiment corresponds to each step in Embodiment 1 one by one, and the specific implementation process thereof is the same, which is not repeated here.

Embodiment 3

[0070] This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps in the image denoising method described in the first embodiment above.

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 provides an image denoising method and system. The method comprises the following steps: acquiring a noise image; inputting the noise image into a denoising model to obtain a denoised image; wherein the denoising model performs feature extraction on the noise image through the multi-scale feature extraction block and the Ghost feature extraction block in sequence to obtain noise features, and the noise features are subtracted from the noise image to obtain a denoised image. The image denoising effect is improved, the image denoising efficiency is improved, training parameters are reduced, and the network structure training time is shortened.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image denoising method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Image denoising is an indispensable step in many image processing problems, and it is widely used in biology, medicine and military fields. Therefore, it is very important to improve the effect of image denoising. Traditional image denoising methods have complex models and contain many parameters that need to be manually tuned. Deep learning has been applied to image denoising because of its powerful learning ability to improve the shortcomings of traditional image denoising methods. [0004] In particular, methods based on convolutional neural networks show strong performance on image denoising. However, the memory consumption of the d...

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): G06T5/00G06V10/44G06V10/774G06V10/80G06V10/82G06N3/04G06N3/08G06K9/62
CPCG06N3/08G06T2207/20081G06T2207/20084G06N3/045G06F18/253G06F18/214G06T5/70
Inventor 李天平冯凯丽李萌韩宇
Owner SHANDONG NORMAL UNIV
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