An image and video enhancement method based on multi-branch convolutional neural network

A convolutional neural network, video enhancement technology, applied in the field of computer vision and image processing, to achieve high-quality video enhancement effects, avoid artifacts and flickering effects

Active Publication Date: 2018-12-11
BEIHANG UNIV
View PDF1 Cites 47 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Among the five types of methods, the first four methods belong to the traditional enhancement methods, and the effect is far behind the deep learning method

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
  • An image and video enhancement method based on multi-branch convolutional neural network
  • An image and video enhancement method based on multi-branch convolutional neural network
  • An image and video enhancement method based on multi-branch convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] The specific implementation of the present invention will be described in detail below with reference to the accompanying drawings. In this example, a picture enhancement (encoding format: JPG) that is underexposed due to low ambient light is selected for detailed description.

[0050] The present invention proposes an image or video enhancement method based on a neural network, which can obtain high-quality realistic enhancement effects. This method has no additional requirements for the system, and any color picture or video can be used as input. At the same time, this method can effectively improve the stability of neural network training and promote the rapid convergence of neural network by proposing a specific target loss function.

[0051] See figure 1 The composition diagram of the multi-branch convolutional neural network processing module of the present invention. The input module of the network first reads the low-light image or video that needs to be processed, ...

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 and video enhancement method based on a multi-branch convolutional neural network, which comprises the following steps: inputting a single image or video sequence of low quality to stably solve the enhanced image or video. A novel multi-branch convolutional neural network structure can effectively solve the problem of image or video quality degradation caused by insufficient illumination, noise and other factors. A novel training loss function can effectively improve the accuracy and stability of the neural network. One application of the invention is unmanned vehicle (aircraft) driving. The principle of the invention is to process and enhance the image quality degradation caused by the surrounding environment change or interference of the video sensor, thereby providing higher quality image and video information for the decision-making system, thereby facilitating the decision-making system to make more accurate and correct decisions. The invention canalso be widely used in the fields of video communication, automatic navigation, video monitoring, short video entertainment, social media, image restoration and the like.

Description

Technical field [0001] The invention relates to the field of computer vision and image processing, in particular to an image and video enhancement method based on a multi-branch convolutional neural network. Background technique [0002] As a fundamental problem in the field of image processing, image enhancement is of great significance to many computer vision algorithms that rely on high-quality images and videos. Existing computer vision algorithms mostly process high-quality pictures or videos. However, in actual applications, it is difficult to obtain high-quality images and videos due to changes in costs and natural conditions. In this case, the image enhancement algorithm can be used as the preprocessing process of the computer vision algorithm to improve the quality of the input image and video of the computer vision algorithm, thereby improving the accuracy of the computer vision algorithm and generating practical application value. [0003] In recent years, deep learning...

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
CPCG06T5/007G06T2207/10016G06N3/045
Inventor 陆峰吕飞帆赵沁平
Owner BEIHANG UNIV
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