Open AccessHybrid image denoising method based on non‐subsampled contourlet transform and bandelet transform
Author(s) -
Wang Xiaokai,
Chen Wenchao,
Gao Jinghuai,
Wang Chao
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0647
Subject(s) - contourlet , wavelet transform , artificial intelligence , pattern recognition (psychology) , s transform , harmonic wavelet transform , stationary wavelet transform , mathematics , computer vision , second generation wavelet transform , top hat transform , wavelet , discrete wavelet transform , computer science , image processing , image (mathematics) , feature detection (computer vision)
The second generation bandelet transform uses the two‐dimensional (2D) separable wavelet transform to improve its image denoising and compression performance. However, the 2D separable wavelet transform is not a shift‐invariant transform and therefore cannot capture geometric information well. The authors propose a hybrid image denoising method in which the 2D separable wavelet transform in the second generation bandelet transform is replaced with the non‐subsampled contourlet transform. The results of the application of the proposed method to several greyscale and colour benchmark images contaminated with various levels of Gaussian white noise and Poisson noise indicate that the proposed method has good peak signal‐to‐noise ratio and visual quality performance.