De-noising and Recovering Images Based on Kernel PCA Theory

Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis are called principal components. Since Kernel PCA is just a PCA in feature space F , the projection of an image in input space can be reconstructed from its principal components in feature space. This enables us to perform several applications concerning de-noising and recovering images. Because of the superiority of Kernel PCA over linear PCA, we also get satisfactory effects of de-noising images using Kernel PCA. Keywords Kernel PCA; principal components; feature space; de-noising and recovering

• Type: Conference
• Year: 2004
• Where: WSCG
• Authors: Pengcheng Xi, Tao Xu
• Keywords: Feature Space, Kernel PCA, Principal Components, WSCG 2004