Earticle

초록

영어

At present, greatest outdoor video-surveillance, optical remote sensing systems and driver-assistance have been designed to work under decent visibility weather conditions. Less visibility often happens weather of hazy or foggy conditions and can strongly influence accuracy or even general functionality of such vision systems. Fog reduces the visibility of a scene and thus the performance of numerous algorithms of computer vision which use feature knowledge. Fog formation is the function of the depth. Estimation of depth knowledge is under constraint problem if single image is presented. Hence, fog removal need assumptions or prior information. In this paper, present a novel algorithm for fog or haze removal purpose with combination of edge enhancement method, color enhancement method, adjustable empirical function and also Wiener filter for efficient outcomes.

목차

Abstract
 1. Introduction
 2. Image Restoration
 3. Deblurring Technique
  A. Lucy- Richardson Algorithm Technique:
  B. Neural Network Approach:
  C. Blind Deconvolution Technique:
  D. Deblurring With Blurred/Noisy Image Pairs:
  E. Deblurring With Motion Density Function:
  F. Deblurring With Handling Outliers:
  G. Deblurring by ADSD-AR:
 4. Literature Survey
 5. Conclusion
 References

키워드

저자

• Ekta Chauhan [ Dept of computer science Engg. Maharana Pratap College Of Technology Gwalior, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605