BM3D mridenoising equipped with noise invalidation technique

Block-matching and 3D filtering (BM3D) has shown a great success in image denoising. In this work we propose a new denoising approach for Magnetic Resonance Imaging (MRI) based on a modified BM3D algorithm. BM3D is a combination of nonlocal approach, 3D wavelet shrinkage, and 3D Wiener filtering. We improve the wavelet thresholding stage of BM3D using Noise Invalidation Denoising (NIDe) technique. The new approach provides the optimum wavelet threshold automatically and adaptive to the statistical characteristics of the available data. This is an advantage over the existing denoising stage of BM3D that currently uses an adhoc thresholding value. Combining the proposed BM3D approach with Variance Stabilization Transformation (VST) enables the use of the proposed method for Magnetic Resonance (MR) Image denoising. In our Simulations, the proposed method outperforms the state of the art BM3D based MRI denoising methods in the sense of PSNR and SSIM for T1, T2 and PD weighted MR images.