In this paper, we propose a novel Scale Noise Level Estimation method based on Double-Density Dual Tree Complex Wavelet Transform (DD_DTCWT), which is referred to as DD_DTCWT_SNLE, to take the advantage of the correlation between the noise and noisy coefficients of DD_DTCWT. The novel DD_DTCWT_SNLE method is formulated through both theoretical analysis and numerical simulation, and is applied into three different threshold de-noising schemes respectively. Simulation results show that there is an approximate linear relation between DD_DTCWT_SNLE and the noise level and that DD_DTCWT_SNLE can reflect the noise level of coefficients in each layer more accurately. The proposed method outperforms the bivariate shrinkage algorithm and a gain of 0.8 dB in PSNR is obtained when compared to other DD_DTCWT based algorithms. We also show the universal applicability of our DD_DTCWT_SNLE for multi-scale linear operators, and its usage as a noise level estimator for all the other linear multi-scale decomposition coefficients.
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- A DD_DTCWT Image De-noising Method Based on Scale Noise Level Estimation
- Springer Berlin Heidelberg
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