Cargando…
Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction
Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired b...
Autores principales: | Wu, Liang, Hu, Shunbo, Liu, Changchun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8112927/ https://www.ncbi.nlm.nih.gov/pubmed/34054939 http://dx.doi.org/10.1155/2021/5577956 |
Ejemplares similares
-
Introducing Swish and Parallelized Blind Removal Improves the Performance of a Convolutional Neural Network in Denoising MR Images
por: Sugai, Taro, et al.
Publicado: (2021) -
Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising
por: Zhang, Shuo, et al.
Publicado: (2023) -
A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising
por: Yao, Li
Publicado: (2020) -
Method for Quantum Denoisers Using Convolutional Neural Network
por: Kim, Bong-Hyun, et al.
Publicado: (2022) -
Multi-View Image Denoising Using Convolutional Neural Network
por: Zhou, Shiwei, et al.
Publicado: (2019)