Comparisons of Several New De-noising Methods for Medical Images
Noise is inevitably introduced to medical images because of various factors in medical imaging. The noise in medical images degrades the quality of images, blurring boundaries and suppressing structural details, thus bring difficulties to medical diagnosis. Therefore, the key to medical image de-noising is to remove the noise while preserving important features. In this paper, we analyze and compare three kinds of representative medical image de-noising algorithms including anisotropic diffusion filtering, bilateral filtering and the sparse representation(SR) based method to provide convenience for targeted choosing of de-noising methods. And the results show: with the noise increasing, the image de-noised by the SR based method always has higher PSNR than that of the other methods, but loses more details. Moreover SR based method takes too long time while anisotropic diffusion filtering takes the shortest time.
medical image de-noising anisotropic diffusion filtering bilateral filtering sparse representation
Lu Zhang Jiaming Chen Yuemin Zhu Jianhua Luo
College of Life Science & Technology,Shanghai Jiao Tong University,200240,Shanghai,P.R.China CREATIS,CNRS UMR 5515 & INSERM unit 630,69621 Villeurbanne cedex,France
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）