自编码
降噪
噪音(视频)
人工智能
均方误差
计算机科学
模式识别(心理学)
高斯噪声
编码器
领域(数学)
信号(编程语言)
高斯分布
噪声测量
算法
计算机视觉
数学
统计
图像(数学)
人工神经网络
物理
量子力学
纯数学
程序设计语言
操作系统
作者
M. Ethan MacDonald,Eremiahs Fikre,Fernando Vega,Abdoljalil Addeh
摘要
In this work, the denoising autoencoder is applied to simulated low field, low resolution, low signal-to-noise images and used to recover the high field, high resolution, high signal-to-noise paired image. Different types of noise, gaussian and chi-squared, is added in simulation. We found that the denoising autoencoder worked slightly better for normally disturbed noise, but not in all cases. We found a linear trend between the model performance with RMSE and the standard deviation of the added noise. This work demonstrates the use of simple and robust denoising autoencoder to improve low field MRI.
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