Medical image denoising by parallel non-local means-Reference-Cited by-同舟云学术

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Medical image denoising by parallel non-local means

Published:2016-06 Issue: Volume:195 Page:117-122
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Mingliang Xu,Pei Lv,Mingyuan Li,Hao Fang,Hongling Zhao,Bing Zhou,Yusong Lin,Liwei Zhou

Funder

National Natural Science Foundation of China (NSFC)

China Post-doctoral Science Foundation

National Key Technology Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference18 articles.

1. Spatially adaptive wavelet thresholding with context modeling for image denoising;Chang;IEEE Trans. Image Process.,2000

2. A joint inter-and intrascale statistical model for Bayesian wavelet based image denoising;Pizurica;IEEE Trans. Image Process.,2002

3. Hybrid inter-and intra-wavelet scale image restoration;Zhang;Pattern Recognit.,2003

4. Multiscale LMMSE-based image denoising with optimal wavelet selection;Zhang;IEEE Trans. Circuits Syst. Video Technol.,2005

5. Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising;Pizurica;IEEE Trans. Image Process.,2006

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1. Deep residual learning-based denoiser for medical X-ray images;Evolving Systems;2024-09-06

2. AsymUNet: An Efficient Multi-Layer Perceptron Model Based on Asymmetric U-Net for Medical Image Noise Removal;Electronics;2024-08-12

3. Performance Analysis of Non-Local Means Denoising on Medical Images and the Impact of Filter Parameter Variation;2024 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET);2024-03-21

4. Non-local means denoising of CT, MRI and US images: An investigation of performance under varying noise levels;AIP Conference Proceedings;2024

5. Images denoising for COVID-19 chest X-ray based on multi-scale parallel convolutional neural network;Multimedia Systems;2023-09-11

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