Earticle

초록

영어

As the state-of-art denoising method, BM3D is capable of achieving good denoising performance by exploiting both the non-local characteristics and sparsity prior knowledge of images. Nevertheless, experimental results show that the dissimilarity measurement defined in BM3D sometimes results in grouping patches with distinct structure. Inspired by the fact about the different impact of noise on patches with various structures, we propose a structure-adaptive image denoising method with 3D collaborative filtering by optimizing the block matching procedure. In our method, the similarity in the variance between patches is incorporated in block matching procedure. Besides, based on the prior knowledge of correlation among patches in the same neighborhood, the spatial distance between the reference patch and the candidate is also taken into account when measuring patches’ dissimilarity. Several numerical experiments demonstrate that the proposed approach achieve better results in PSNR and visual effect than original BM3D.

목차

Abstract
 1. Introduction
 2. Related Work about BM3D
 3. Structure-adaptive BM3D Denoising Scheme (SA-BM3D)
 4. Simulation Results
 5. Conclusion
 ACKNOWLEDGMENTS
 References

키워드

저자

• Xuemei Wang [ College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China ]
• Dengyin Zhang [ Internet of Things Technology Park, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China ]
• Min Zhu [ Information & Technical Department, Wenzhou Vocational & Technical College, wenzhou 325000 ]
• Yingtian Ji [ Internet of Things Technology Park, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China ]
• Jin Wang [ College of Information Engineering, Yangzhou University, Yangzhou 225009, 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