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

Adaptive global dark channel prior image dehazing method for bright area

A dark channel prior and bright area technology, which is applied in the field of adaptive global dark channel prior defogging, can solve the problems of high computational complexity and failure of the Halo phenomenon

Inactive Publication Date: 2016-07-20
GUILIN UNIV OF AEROSPACE TECH
View PDF5 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0019] In view of the failure of the dark channel prior defogging algorithm for bright areas, and the problems of block effect, Halo phenomenon and high computational complexity in obtaining dark channel by blocks, the present invention proposes an adaptive global dark channel prior for bright areas. defogging 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
  • Adaptive global dark channel prior image dehazing method for bright area
  • Adaptive global dark channel prior image dehazing method for bright area
  • Adaptive global dark channel prior image dehazing method for bright area

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0078] The fuzzy domains of the two input variables are normalized to the [0, 1] range, and the domains of the output variables K and ω are [0.2, 0.6] and [0.6, 1] respectively; except that the input variable P adopts Gaussian membership Except for degree function, other variables are described by triangular or trapezoidal membership function.

[0079] The fuzzy logic rules are shown in Table 1. The linguistic values ​​of each input and output variable are set to three, which are S, M and B respectively. S represents small, M represents medium, and B represents large. There are 9 fuzzy logic rules in the table. Rules 1-3 indicate that when the P value is small, that is, the fog concentration is high, the output tolerance K is small and the adjustment factor ω is large regardless of the coverage of the bright area; Rules 7-9 indicate that the P value is large, which means that the fog is thin and the image contrast is better. The values ​​of K and ω are mainly determined by the...

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 adaptive global dark channel prior dehazing method for a bright area. The method comprises the following steps; 1, a global dark channel value of a haze image I(x) is solved; 2, a fuzzy logic controller is built to obtain a tolerance parameter and a transmittance adjustment factor; 3, the tolerance parameter is used for dividing the haze image into a bright area and a non-bright area; 4, the atmospheric light intensity A is solved; 5; the transmittance is solved; and 6, the tolerance parameter, the transmittance and the atmospheric light intensity are used to obtain a restored image. Problems of block effects, Halo phenomena, color distortion and the like caused by bright area distortion and block processing can be effectively solved, good dehazing effects can be acquired in a condition of not increasing exposure treatment, objective evaluation results for three aspects of the image contrast ratio, the information entropy and the average gradient are obviously better than those of other comparison algorithms, and the operation efficiency is greatly enhanced.

Description

technical field [0001] The invention relates to digital image processing, in particular to model-based image fog, and more specifically to an adaptive global dark channel prior defogging method for bright regions. Background technique [0002] There are many types of model-based image defogging algorithms, which can be divided into two types: the defogging method based on multiple images and the defogging method based on a single image according to the different processing objects. The former method estimates the properties of the propagation medium by comparing and analyzing multiple images of the same scene under different weather conditions, so it has high requirements on the imaging system and is not suitable for real-time processing; while the defogging algorithm based on a single image can adapt to Various applications have become the hotspot of current research. Tan et al achieved the purpose of dehazing by maximizing the local contrast of the restored image [1] , b...

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): G06T5/00G06T5/40
CPCG06T5/40G06T2207/20004G06T5/73
Inventor 邓莉李欧迅赵素文孙山林嵇建波杨双周菊瑄陈锡华梁强张文凯王勇军盘书宝张绍荣
Owner GUILIN UNIV OF AEROSPACE 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