Earticle

초록

영어

In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper. Firstly, this algorithm is adopted to obtain self-adaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistical features of each atom in the dictionary are extracted to form the feature set, and meanwhile the feature set of the atoms is adopted to divide the atoms into two types (non-noisy atoms and noisy atoms); finally, the non-noisy atoms are adopted to recover the image, thus to realize the de-nosing purpose. The experiment result shows: the proposed algorithm does not need to know the prior information of the noise and PSNR performance thereof is better than that of existing algorithms, and meanwhile the proposed algorithm can well keep the image detail and texture structure information, thus to improve visual effect.

목차

Abstract
 1. Introduction
 2. Dictionary Learning Technology
  2.1. Hyperspectral Image De-Noising Algorithm Based on Multitask Nonnegative Dictionary Learning
  2.2. Multitask Nonnegative Dictionary Learning Model
  2.3. Solution of Multitask Nonnegative Dictionary Learning Model
  2.4. Hyperspectral Remote Sensing Image De-Noising
 3. Detailed Algorithm Steps and Analysis
  3.1. Detailed Algorithm Steps
 4. Test and Analysis
 5. Conclusion
 References

키워드

저자

• Li Weizheng [ Department of Communication Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, 211800, 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