Adaptive noise filtering of white-light confocal microscope images using Karhunen-Loève expansion Conference

Balasubramanian, M, Iyengar, SS, Wolenski, P et al. (2005). Adaptive noise filtering of white-light confocal microscope images using Karhunen-Loève expansion . SMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XI, 5909 1-12. 10.1117/12.615252

cited authors

  • Balasubramanian, M; Iyengar, SS; Wolenski, P; Reynaud, J; Beuerman, RW



  • We present a noise filtering technique using Karhunen-Loève expansion by the method of snapshots (KLS) using a small ensemble of 3 images. The KLS provides a set of basis functions which comprise the optimal linear basis for the description of an ensemble of empirical observations. The KLS basis is computed using the eigenvectors of the covariance matrix R of the ensemble of images. The significance of each of the basis functions is determined by the magnitude of the corresponding eigenvalues of R, the largest being the most significant. Since all the three images in the ensemble represent the same scene and are registered, the KLS basis construct using the eigenvectors of R with the least eigenvalues typically represent the non-significant and uncommon features in the ensemble. We show that most of the noise in the scene can be removed by reconstructing the image using the KLS basis function constructed using the eigenvector of R with the largest eigenvalue. R is 3x3 symmetric positive definite matrix and hence has a full set of orthogonal eigenvectors. The KLS filtering scheme described here is faster and does not require prior knowledge about the image noise. We show the performance of the proposed method on the images of random cotton fibers acquired using white-light confocal microscope (WLCM) and compare the performance with a median filter. Also, we show that a simple inverse-filter deconvolution algorithm provides an impressive image restoration by pre-filtering the images using the proposed KLS filtering technique.

publication date

  • December 1, 2005

Digital Object Identifier (DOI)

start page

  • 1

end page

  • 12


  • 5909