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

A CNN denoising method based on parallel feature extraction

A feature extraction and noise technology, applied in the field of image denoising, can solve the problem of unsatisfactory denoising effect

Active Publication Date: 2019-03-01
ANHUI UNIV OF SCI & TECH
View PDF13 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the overall denoising effect obtained by the existing methods is not satisfactory.

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
  • A CNN denoising method based on parallel feature extraction
  • A CNN denoising method based on parallel feature extraction
  • A CNN denoising method based on parallel feature extraction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048]The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0049] Such as figure 2 As shown, the present invention discloses a CNN denoising method based on parallel feature extraction, including six steps. Step S1, build a CNN denoising network model for parallel feature extraction; Step S2, initialize the training parameters of the CNN denoising network model; Step S3, build a training set; Step S4, design a loss function, and aim to minimize the loss function Train the CNN denoising network model to obtain the CNN...

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 discloses a CNN denoising method based on parallel feature extraction. The CNN denoising method comprises the six steps of 1, building a CNN denoising network model for parallel featureextraction; Step 2, initializing training parameters of the CNN denoising network model; Step 3, constructing a training set; 4, designing a loss function, and training a CNN denoising network model by taking a minimum loss function as a target to obtain a CNN denoising model; 5, taking the noise image as the input of a CNN denoising model, wherein the output of the noise image is the noise information learned by the network model; and 6, subtracting the noise information learned in the step 5 from the noise image to obtain a denoised clean image. According to the method, the noise can be completely removed, the texture information of the image can be well reserved, and the objective indexes PSNR and SSIM are remarkably improved.

Description

technical field [0001] The invention relates to the field of image denoising, in particular to a CNN denoising method based on parallel feature extraction. Background technique [0002] In reality, digital images are often affected by imaging equipment and external environmental noise interference during digitization and transmission, which are called noisy images or noisy images. The ultimate goal of image denoising is to improve a given image and solve the problem of image quality degradation caused by noise interference in actual images. Denoising technology can effectively improve the image quality, increase the signal-to-noise ratio, and better reflect the information carried by the original image. As an important preprocessing method, people have conducted extensive research on image denoising algorithms. [0003] At present, there are many classic methods for image denoising, but they can be roughly divided into two categories, one is based on spatial domain filterin...

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): G06T5/00G06N3/04
CPCG06T2207/20081G06T2207/20084G06N3/045G06T5/70
Inventor 赵佰亭贾晓芬郭永存黄友锐柴华荣
Owner ANHUI UNIV OF SCI & 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