z-logo
open-access-imgOpen Access
Denoising of Multi-Modal Images with PCA Self-Cross Bilateral Filter
Author(s) -
Yu Qiu,
Kiichi Urahama
Publication year - 2010
Publication title -
ieice transactions on fundamentals of electronics communications and computer sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.188
H-Index - 52
eISSN - 1745-1337
pISSN - 0916-8508
DOI - 10.1587/transfun.e93.a.1709
Subject(s) - artificial intelligence , noise reduction , principal component analysis , modal , computer vision , bilateral filter , filter (signal processing) , computer science , pattern recognition (psychology) , image denoising , wavelet transform , wavelet , image (mathematics) , materials science , polymer chemistry
We present the PCA self-cross bilateral filter for denoising multi-modal images. We firstly apply the principal component analysis for input multi-modal images. We next smooth the first principal component with a preliminary filter and use it as a supplementary image for cross bilateral filtering of input images. Among some preliminary filters, the undecimated wavelet transform is useful for effective denoising of various multi-modal images such as color, multi-lighting and medical images.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom