In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.

中文翻译：

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一种利用深度残差网络进行MR图像去噪的多特征提取方法

为了提高磁共振（MR）图像的分辨率，减少噪声的干扰，提出了一种基于深度残差网络的多特征提取去噪算法。首先，通过组合三个不同大小的卷积核构建特征提取层，用于获取多个浅层特征进行融合，增加网络的多尺度感知能力。然后结合批量归一化和残差学习技术对深度网络进行加速和优化，同时解决深度学习中的内部协变量迁移问题。最后，通过结合感知损失和传统均方误差损失来定义联合损失函数。当网络被训练时，它不仅可以在像素级别进行比较，还可以在更高级别的语义特征上进行学习，以生成更清晰的目标图像。基于MATLAB仿真平台，使用TCGA-GBM和CH-GBM数据集对所提出的算法进行实验验证。结果表明，当图像大小设置为优化算法为Adam，所提算法的性能最好，去噪效果明显优于其他对比算法。尤其是在高强度噪声水平下，去噪优势更加突出。