A fast image de-smog system based on depth convolution neural network

A deep convolution and neural network technology, applied in the field of image processing, can solve the problems of inconsistent expectations, gray photos, affecting the quality of pictures, etc., to achieve the effect of real-time application and good haze removal effect.

Inactive Publication Date: 2019-02-15
SICHUAN CHANGHONG ELECTRIC CO LTD
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Smog has become the norm. No matter which city you live in, smog can only be classified as severe or severe. So the pictures taken outdoors on a smoggy day will often seriously affect the quality of the picture due to the weather, and the photos taken show a kind of gray. Misty effect, not as expected

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 fast image de-smog system based on depth convolution neural network
  • A fast image de-smog system based on depth convolution neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other implementations obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0027] Such as Figure 1-2 As shown, a fast image haze removal system based on a deep convolutional neural network, constructs an efficient single image haze removal depth convolutional neural network model, and a haze image training set, and trains the network proposed by the present invention, Finally, a clear haze-free image is reconstructed, which specifically includes the following steps:

[0028] Step 1: Construct a high-performance, fast single image ha...

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 fast image smog removing system based on a depth convolution neural network, which constructs an efficient single image smog removing depth convolution neural network model and a smog image training set, trains the network proposed by the invention, and finally reconstructs a clear smog-free image. By constructing deep convolution neural network model and atmospheric scattering model and training them in depth, the smog removal effect can be obtained quickly and the real-time application can be realized. The system of the present invention can be easily extended to other image or video processing fields, such as image or video deblurring, image or video restoration, and the like.

Description

technical field [0001] The invention relates to the technical problem of haze removal in the field of image processing, in particular to a fast image haze removal system based on a deep convolutional neural network. Background technique [0002] In recent years, air pollution has been serious and air quality has gradually declined. Smog is the result of the interaction between specific climate and human activities. The economic production and social activities of a high-density population will emit a large amount of fine particles. Once the emission exceeds the atmospheric circulation and carrying capacity, the suspended particles will continue to accumulate, and large-scale smog will easily appear. Smog has become the norm. No matter which city you live in, smog can only be classified as severe or severe. So the pictures taken outdoors on a smoggy day will often seriously affect the quality of the picture due to the weather, and the photos taken show a kind of gray. The m...

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): G06T5/00
CPCG06T5/003G06T2207/20081G06T2207/20084
Inventor 何娟刘蛟万蕾
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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