Earticle

초록

영어

This paper conducts a research on information loss of local feature existing in the image denoising process and puts forward the method of image sparse denoising of redundant dictionary based on filtering guidance. This method utilizes bias noise (additional noise and image errors after denoised image and the corresponding additional noise deviation) for image sparse expression, and extracts the feature information of bias noise to improve the effectiveness of de-noising. In the first place, based on filtering guidance, the method carries out aftertreatment to bias noise still existing after denoise the image. And then, the method, in the basis of this bias noise, designs a new dictionary training method, and obtains redundant dictionary for image processing through self-adaption. Finally, the method extracts featured texture from bias noise image based on the dictionary mentioned above. And it takes advantage of filtering guidance in combination with featured texture extracting information and denoising image to realize image restoration. According to emulated data, the performance of proposed algorithm should be better than the selected comparing algorithm and be equipped with a better visual recovery effect.

목차

Abstract
 1. Introduction
 2. Sparse Dictionary Representation
  2.1. Filtering Guidance
  2.2. Dictionary Training
 3. Sparse Denosing of Bias Noise
  3.1. Image Bias Noise
  3.2. Redundant Dictionary Training
  3.3. Image Denosing
 4. Algorithm Description
  4.1. Procedures of Algorithm
  4.2. Convergence Analysis
 5. Experimental Analysis
 6. Conclusion
 Acknowledgment
 References

키워드

저자

• Yu Xiang [ Department of Information Technology,Tianjin Chengjian University, Tianjin City, China ]

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