Abstract

An image denoising method in moment domain has been proposed. The denoising involves the development and evaluation based on the modified nonlocal means (NLM) algorithm. It uses the similarity of the neighborhood, evaluated using Krawtchouk moments. The results of the proposed denoising method have been validated using peak signal-to-noise ratio (PSNR), a well-known quality measure such as structural similarity (SSIM) index and blind/referenceless image spatial quality evaluator (BRISQUE). The denoising algorithm has been evaluated for synthetic and real clinical images contaminated by Gaussian, Poisson, and Rician noise. The algorithm performs well compared to the Zernike based denoising as indicated by the PSNR, SSIM, and BRISQUE scores of the denoised images with an improvement of 3.1 dB, 0.1285, and 4.23, respectively. Further, comparative analysis of the proposed work with the existing techniques has also been performed. It has been observed that the results are competitive in terms of PSNR, SSIM, and BRISQUE scores when evaluated for varying levels of noise.

© 2015 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Speckle denoising by variant nonlocal means methods

Yassine Tounsi, Manoj Kumar, Abdelkrim Nassim, Fernando Mendoza-Santoyo, and Osamu Matoba
Appl. Opt. 58(26) 7110-7120 (2019)

Speckle noise reduction in digital speckle pattern interferometric fringes by nonlocal means and its related adaptive kernel-based methods

Yassine Tounsi, Manoj Kumar, Abdelkrim Nassim, and Fernando Mendoza-Santoyo
Appl. Opt. 57(27) 7681-7690 (2018)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (4)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (7)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (20)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription