Earticle

초록

영어

In allusion to remove Racian noise while lessen the loss of details as low as possible, this paper proposed an filter algorithm which comprehensive utilize Multi-Objective Genetic Algorithm (MOGA) and Shearlet transform based on a Multi-scale Geometric Analysis (MGA) theory. First, it performs a wavelet multi-scale decomposition of image. Then, it builds target function in MOGA by several evaluation methods such as Signal to Noise Ratio (SNR). Third, it uses the MOGA to optimal coefficients of Shearlet wavelet threshold value in different scale and different orientation. Finally, it obtains the composite image by using inverse lifting wavelet transform. Experimental results show tha our proposed new algorithm presented here is more effective in removing Rician noise, and giving better Peak Signal Noise Ratio (PSNR) gains, without manual intervention in comparison with other traditional filters.

목차

Abstract
 1. Introduction
 2. Related Theories
  2.1. Rician Noise
  2.2. Shearlet Transform
 3. Proposed Algorithm
  3.1. Threshold Rule
  3.2. Target Function
  3.3. Proposed Model
 4. Experimental Results and Analysis
  4.1. Evaluation index
  4.2. Experimental Results
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Zhiyong Fan [ Nanjing University of Science & Technology, Nanjing University of Information Science & Technology ]
• Quansen Sun [ Nanjing University of Science & Technology ]
• Feng Ruan [ Nanjing University of Information Science & Technology ]
• Yiguang Gong [ Nanjing University of Information Science & Technology ]
• Zexuan Ji [ Nanjing University of Science & Technology ]

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