Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising.

ABSTRACT: Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network

SUBMITTER: Yu H 

PROVIDER: S-EPMC6650831 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature