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

Underwater image real-time enhancement method based on conditional generative adversarial network

A conditional generation and underwater image technology, applied in the field of image processing, can solve the problems of underwater image noise, distortion, etc., and achieve the effect of improving the enhancement ability

Active Publication Date: 2020-04-24
NANJING INST OF TECH
View PDF8 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to provide a real-time enhancement method of underwater images based on conditional generation confrontation network, which solves the technical problems of noise and distortion of underwater images

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
  • Underwater image real-time enhancement method based on conditional generative adversarial network
  • Underwater image real-time enhancement method based on conditional generative adversarial network
  • Underwater image real-time enhancement method based on conditional generative adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] Such as Figure 1-Figure 4 A real-time enhancement method for underwater images based on a conditional generation confrontation network is shown, including the following steps:

[0042] Step 1: Establish a conditional generative confrontation network system in the robot vision system. The conditional generative confrontation network system includes an image acquisition module, a domain module, a network model module and a discriminator module;

[0043] The image acquisition module is used to obtain the original image, and the domain module is used to input the source domain X and output the expected domain Y on the original image, the source domain X is the distorted image area in the original image, and the expected domain Y is the enhanced image; The goal of the present invention is to learn the mapping G:X→Y to perform automatic image enhancement.

[0044] The present invention adopts a model based on conditional generative adversarial networks, in which the generat...

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 an underwater image real-time enhancement method based on a conditional generative adversarial network. The invention belongs to the technical field of image processing, the method comprises the steps of establishing a conditional generative adversarial network system in a robot vision system, an image model network is established by following the principle of U-net; establishing a generative adversarial network architecture by adopting a Markov data chain through a discriminator module; the technical problems of noise and distortion of the underwater image are solved;the generative adversarial network is optimized by using a deep convolution method; the visual image enhancement capability of the underwater robot is improved; according to the underwater robot imageenhancement method based on the deep convolution generative adversarial network, the perception quality of the image is evaluated based on the overall content, color, local texture and style information of the image, and a multi-modal target function is formulated to train the model.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a real-time underwater image enhancement method based on a conditional generation confrontation network. Background technique [0002] Autonomous underwater robots are widely used in marine environment monitoring, submarine cable inspection, underwater scene analysis, and submarine topographic mapping. An important problem encountered in the work of underwater robots is that the robot vision system will be seriously affected by low visibility, light refraction, absorption and scattering, and these optical artifacts will cause nonlinear distortion in the captured image, seriously affecting the underwater based Performance of robotic vision systems such as underwater object tracking, detection and classification, segmentation and visual servoing. Fast and accurate image enhancement techniques can alleviate these problems by restoring the perceptual and statistical propertie...

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): G06T5/00G06N3/08G06N3/04
CPCG06N3/088G06T2207/20081G06T2207/20084G06T2207/30181G06N3/045G06T5/90G06T5/70
Inventor 陈巍陈丝雨陈国军
Owner NANJING INST OF 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