In standard wavelet methods

the empirical wavelet coefficients are thresholded term by term

on the basis of their individual magnitudes. Information on other coefficients between the different scales and in the same scale has no influence on the treatment of particular coefficients

resulting in the lower accuracy of signal estimation. A translation-invariant (TI)neighboring-coefficients thresholding is designed by incorporating the different evolution of signal and noise along the scales of wavelet domain and information in the same scale. Considering the particularity of noise in magnetic resonance (MR)images

a novelty MR complex denoising algorithm based on TI neighboring-coefficients thesholding is developed by employing the complex entity method in MR complex images. The results of the simulated experiments show that the proposed algorithm has the higher accuracy of signal estimation

and outperforms previous MRI denoising methods about denoising capability.