Patch-based denoising with K-nearest neighbor and SVD for microarray images

1Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Irrespective of certain major advancement in filtering process in medical images, the denoising operation in microarray images are still considered to be unsolved and offers a large scope of research. Existing denoising principles are less investigated on such complex and massive dimensional microarray image that leads to the development of the proposed system. We present a method of performing simple denoising operation considering the presence of Gaussian noise in microarray image. From the target image denoising method, an improved version of patch-based denoising approach has been developed considering various forms of distance-based matching methods. The study outcome of the proposed system has been found to offer better peak signal-to-noise ratio and structural similarity index in contrast to existing filtering techniques.

Cite

CITATION STYLE

APA

Elavaar Kuzhali, S., & Suresh, D. S. (2019). Patch-based denoising with K-nearest neighbor and SVD for microarray images. In Advances in Intelligent Systems and Computing (Vol. 763, pp. 132–147). Springer Verlag. https://doi.org/10.1007/978-3-319-91186-1_15

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free