降噪
人工智能
深度学习
计算机科学
噪音(视频)
卷积神经网络
模式识别(心理学)
相似性(几何)
信噪比(成像)
人工神经网络
图像(数学)
电信
作者
Stephen H. Gregory,Hu Cheng,Sharlene D. Newman,Yu Gan
摘要
The state-of-the-art methods of Magnetic Resonance Imaging (MRI) denoising technologies have improved significantly in the past decade, particularly those based in deep learning. However, the major issues in deep learning based denoising algorithms is both that the model architectures are not built for the complex noise distributions inherent in MRI, and that the data given to these algorithms is typically synthetic, and thus, they fail to generalize to spatially variant noise distributions. The noise varies greatly dependent upon such factors as pulse sequence of the MRI sequence, reconstruction method, coil configuration, physiological activities, etc. To overcome these issues, we have created HydraNet, a multi-branch deep neural network architecture that learns to denoise MR images at a multitude of noise levels, and which has critically been trained using only real image pairs of high and low signal-to-noise ratio (SNR) images. We prove the superiority of HydraNet at denoising complex noise distributions in comparison to the leading deep learning method in our experimentation, in addition to non-local collaborative filtering-based methods, quantitatively in both Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and qualitatively upon inspection of denoised MRI samples.
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