3D medical image denoising using 3D block matching and low-rank matrix completion

3D Denoising as one of the most significant tools in medical imaging was studied in the literature. However, most existing 3D medical data denoising algorithms have assumed the additive white Gaussian noise. In this work, we propose an efficient 3D medical data denoising method that can handle a noise mixture of various types. Our method is based on modified 2D Adaptive Rood Pattern Search (ARPS) [1] and low-rank matrix completion as follows. In our method, a noisy 3D data is processed in blockwise manner, for each processed 3D block we find similar 3D blocks in 3D data, where we use overlapped 3D patches to further lower the computation complexity. The 3D blocks then will stack together and unreliable voxels will be replaced using fast matrix completion method [2]. Experimental results show that the proposed method is able to robustly denoise the mixed noise from 3D medical data.