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MRI noise estimation and denoising using non-local PCA |
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| Institution: | 1. College of Computer Science, Sichuan University, Chengdu 610065, China;2. Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China;3. Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China;4. School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China;5. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China;6. Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China;7. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China |
| Abstract: | This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases. |
| Keywords: | MRI PCA Denoising Sparseness Non-local means |
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