An efficient local adaptive image denoising algorithm based on the double-density dual-tree complex wavelet transform (DD-DT CWT) is proposed. First

The principles and characteristics of DD-DT CWT are analyzed and the derivation of bivariate shrinkage function(BSF) is given. DD-DT CWT is implemented by applying four 2-D double-density DWTs in parallel to the noise images with distinct filter sets for the rows and columns. According to the statistical properties of wavelet coefficients and the dependency of inter-level and intra-level coefficients

the bivariate shrinkage function with local variance estimation is adopted to processing wavelet coefficients. The denoised images are synthesized using the wavelet coefficients after processing. Finally

The proposed algorithm is tested on some gray and color noisy images. Experimental results show that

compared with the noise images

the Peak Signal-to-Noise Ratio (PSNR) gains reach 11.72dB

Mean Structural Similarity (MSSIM)2.7 times and Composite Peak Signal-to-Noise Ratio (CPSNR) 11.68dB when the noise variance is 30. Meanwhile

the proposed algorithm is more efficient in noise removal and edge reservation for all the noise images with different noise variance

and that the visual quality of the denoised images is improved.