Sensors, Vol. 19, Pages 2918: Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising

Sensors, Vol. 19, Pages 2918: Laplacian Eigenmaps Network-Based Nonlocal Means Method for MR Image Denoising Sensors doi: 10.3390/s19132918 Authors: Houqiang Yu Mingyue Ding Xuming Zhang 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 (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research

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