LAUSR

NeighShrink is an efficient image denoising algorithm for the reduction of additive white Gaussian noise. However, it does not perform well in terms of Rician noise removal for MRI (Magnetic Resonance Imaging). Allowing for the characteristics of squared-magnitude MR (Magnetic Resonance) images, which follow a non-central chi-square distribution, the CURE (Chi-Square Unbiased Risk Estimation) is used to determine an optimal threshold for NeighShrink. Therefore, we propose the NeighShrinkCURE denoising algorithm. Bilateral filtering and cycle spinning are used to further improve denoising performance. Experimental results show that the proposed algorithm is simple and efficient, and provides good noise reduction while preserving edges and details well. Compared with some similar MRI denoising algorithms, the proposed algorithm has improvements in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Although running time of the proposed algorithm has some increment compared with some current MRI denoising algorithms, the comprehensive performance of the proposed algorithm is superior to several current MRI denoising algorithms.