Abstract
This paper introduces an image denoising procedure based on a 2D scale-mixing complex-valued wavelet transform. Both the minimal (unitary) and redundant (maximum overlap) versions of the transform are used. The covariance structure of white noise in wavelet domain is established. Estimation is performed via empirical Bayesian techniques, including versions that preserve the phase of the complex-valued wavelet coefficients and those that do not. The new procedure exhibits excellent quantitative and visual performance, which is demonstrated by simulation on standard test images.
Original language | English |
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Article number | 6918447 |
Pages (from-to) | 5165-5174 |
Number of pages | 10 |
Journal | IEEE Transactions on Image Processing |
Volume | 23 |
Issue number | 12 |
DOIs | |
Publication status | Published - 1 Dec 2014 |
Keywords
- bivariate normal distribution
- complex-valued wavelets
- empirical bayes estimation
- Image denoising
- posterior mean
- scale-mixing wavelet transform
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design