Open AccessWavelet and FFT Based Image Denoising Using Non-linear Filters
Author(s) -
S. Gopinathan,
R. Kokila,
P. Thangavel
Publication year - 2015
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i5.pp1018-1026
Subject(s) - thresholding , noise reduction , wavelet , fast fourier transform , mathematics , non local means , algorithm , filter (signal processing) , artificial intelligence , computer science , image (mathematics) , pattern recognition (psychology) , computer vision , image denoising
We propose a stationary and discrete wavelet based image denoising scheme and an FFTbased image denoising scheme to remove Gaussian noise. In the first approach, high subbands are added with each other and then soft thresholding is performed. The sum of low subbands is filtered with either piecewise linear (PWL) or Lagrange or spline interpolated PWL filter. In the second approach, FFT is employed on the noisy image and then low frequency and high frequency coefficients are separated with a specified cutoff frequency.Then the inverse of low frequency components is filtered with one of the PWL filters and the inverse of high frequency components is filtered with soft thresholding. The experimental results are compared with Liu and Liu's tensor-based diffusion model (TDM) approach.