Earticle

초록

영어

Aiming at the degeneration phenomenon of images taken in mist, according to the features of the degraded images, an improved haze removal algorithm based on genetic fuzzy clustering is presented in this paper after analyzing its defects and shortcomings. Firstly, the improved atmospheric scattering model is established. Secondly, the original image is converted into a standard image through the improved model, and then we present a new multi-scale image edge detection by genetic fuzzy clustering, Based on this observation, we can use the multi-scale image edge detection to estimate the haze thickness directly and recover a high quality haze-free image. The new algorithm uses good global search ability of the genetic algorithm, which will implement the transfer from the scene defogging problem into the optimal estimation problem under global contrast optimal point. Compared with other algorithms for degraded images, the improved haze removal algorithm not only detects image edge precisely, but also has better performance in situations of dense haze. Theoretical analysis and experimental results demonstrate that, the new algorithm improved in this paper are effective for removal of fog-degraded images, and can be applied to the practical situations.

목차

Abstract
 1. Introduction
 2. The Improved Physical Model
 3. Edge Detection based on Genetic Fuzzy Clustering
 4. Depth Region Segmentation
 5. Experimental Results and Performance Analysis
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Xiaoguang Li [ Department of Electrical Engineering and Automation, Luoyang Institute of Science and Technology, Luoyang, 471023, China ]
• Huiying Huang [ Department of Electrical Engineering and Automation, Luoyang Institute of Science and Technology, Luoyang, 471023, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605