Nonlinear tensor diffusion filter for the denoising of CT/MR images Online publication date: Thu, 05-Jan-2023
by S.N. Kumar; A. Lenin Fred; H. Ajay Kumar; P. Sebastin Varghese
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 24, No. 1/2, 2023
Abstract: The partial differential equation based algorithms play a prominent role in image processing and computer vision applications. The anisotropic diffusion technique was widely used for image enhancement and denoising. The Perona-Malika algorithm based on anisotropic diffusion fails to preserve sharp edges and fine details in the denoised image. In this paper, the variants of Perona-Malika (PM) model, nonlinear scalar diffusion (NLSD) filter and nonlinear tensor (NLTD) filter are analysed. The algorithms are analysed on sheep phantom image corrupted with Gaussian and Rician noise and results were validated by performance metrics like PSNR, MAE, EPI and MSSIM. The NLTD filter produces superior results when compared with NLSD and PM filter. The NLTD filter was also found to yield efficient restoration results for real-time CT/MR images and was validated by entropy measure.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com