Earticle

초록

영어

In this paper, we propose an improved method for the removal of additive Gussian white noise from PET-CT images. Different from the traditional sparse representation based denoising methods, our method is composed of two distinctively steps such as the preliminary denoise and the detail compensation. By constructing a sparse representation model, denoising is formulated as an optimization problem that can be solved on an over-complete dictionary. The proposed method effectively trains this dictionary by using K-SVD algorithm with atom replace model. Then the preliminary denoised image is reconstructed through improved OMP algorithm with the fidelity factor of SSIM (Structural Similarity). The detail compensation image is obtained by using the difference between the noisy image and the preliminary de-noised image, and the improved OMP algorithm is employed again to get the denoised detail compensation image. Finally, the final denoised image is reconstructed by adding the denoised detail compensation image to the preliminary denoised image. Experiment results have shown that the proposed method is better than some other denoising methods in terms of PSNR and SSIM.

목차

Abstract
 1. Introduction
 2. Sparse Representation
  2.1. Denoising Model
  2.2 OMP Algorithm
 3. The Proposed Method
  3.1 Structural Similarity
  3.2 Atom substitution Model
  3.3 The Proposed Method
 4. Experiments
 5. Conclusion
 References

키워드

저자

• Zhi Cui [ School of Communication and Electronic Engineering, Hunan City University ]
• Xian-Pu Cui [ School of Communication and Electronic Engineering, Hunan City University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605