A DD_DTCWT Image De-noising Method Based on Scale Noise Level Estimation

• Published in: Advances in Image and Graphics Technologies Vol. 363; pp. 145 - 153
• Main Authors: Xu, Weiling, Wang, Shuwang
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2013; Springer Berlin Heidelberg
• Series: Communications in Computer and Information Science
• Subjects: Bivariate shrinkage function; Double-density dual tree complex wavelet transform; Image de-noising; Scale noise level estimation
• ISBN: 9783642371486; 3642371485
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-642-37149-3_18

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.