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

Mobile phone shot image de-noising method based on dual convolutional networks

A technology for shooting images and convolution, which is applied in the field of image denoising, which can solve the problems of inability to effectively estimate the noise level of images taken by mobile phones, low peak signal-to-noise ratio of denoised images, etc., to solve the problem of greatly reducing the denoising effect and reducing the number of channels , good structural effect

Inactive Publication Date: 2021-04-09
SOUTHWEST PETROLEUM UNIV
View PDF6 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the defects of the existing technology and improve the problem that conventional deep learning methods cannot effectively estimate the noise level of images taken by mobile phones, resulting in low peak signal-to-noise ratio of denoised images, a double convolutional network (TwiceConvolutional Neural Networks, T-CNN) is proposed. ) denoising method, the present invention can improve the denoising effect of the image captured by the mobile phone

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
  • Mobile phone shot image de-noising method based on dual convolutional networks
  • Mobile phone shot image de-noising method based on dual convolutional networks
  • Mobile phone shot image de-noising method based on dual convolutional networks

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The main realization principles, specific implementation methods, etc. of the technical solution of the present invention will be described in detail below.

[0037] 1. Construct a double convolution denoising network model;

[0038] 1) Construct the noise estimation subnetwork:

[0039] Construct three network layers, input layer, hidden layer, and output layer; the input layer is the input of the image taken by the mobile phone; the output layer is the output of the estimated noise; the hidden layer is divided into 5 parts: the first part of the hidden layer is the convolutional layer , the convolution layer contains 64 convolution kernels, the size is 3x3, the step size is 1, the padding is 1, and the activation function is a linear rectification function; the second part of the hidden layer is 3 redundant link blocks, each redundant The co-link block contains two batch normalizations and two convolutional layers. The convolutional kernel size of the first convoluti...

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 mobile phone shot image de-noising method based on dual convolutional networks, which improves the defects of FFDNet, adds a noise estimation sub-network, constructs a redundant link block structure, better saves the structure of an original image, and solves the problem that the de-noising effect is greatly reduced due to the fact that the subjective estimation noise level of FFDNet is wrong.

Description

technical field [0001] The invention relates to the field of image denoising, in particular to images captured by mobile phones, and in particular to a method for denoising images captured by mobile phones based on double convolution networks. Background technique [0002] Today, smartphones are recognized as the most important technology product that affects the daily life of ordinary people. Among the various functions of smartphones, taking pictures is a very important part. Although people are becoming more and more accustomed to using light smartphones to take pictures instead of bulky digital SLR cameras, due to the limitation of the internal space of mobile phones, there is still a gap between the sensor size, aperture size and other hardware configurations and digital SLR cameras. The advantage of smartphones over digital SLRs is that they have powerful chips and neural network processors. Smartphones can use the powerful computing capabilities of their chips to solv...

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): G06T5/00G06N3/04G06N3/08
CPCG06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06N3/045G06T5/73
Inventor 罗仁泽黄雪霁郭亮庹娟娟
Owner SOUTHWEST PETROLEUM 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