School of Computing, Hubei Polytechnic University, Huangshi, Hubei 435003, China.
Comput Math Methods Med. 2020 Nov 5;2020:8823861. doi: 10.1155/2020/8823861. eCollection 2020.
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 190 × 215 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.
为了提高磁共振（MR）图像的分辨率并降低噪声干扰，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，通过结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，加速和优化深度网络，同时解决深度学习中内部协变量转移的问题。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。在网络训练过程中，不仅可以在像素级进行比较，还可以在更高层次的语义特征上进行学习，生成更清晰的目标图像。基于 MATLAB 仿真平台，使用 TCGA-GBM 和 CH-GBM 数据集进行实验验证。结果表明，当图像尺寸设置为 190×215 且优化算法为 Adam 时，所提算法的性能最佳，其去噪效果明显优于其他对比算法。特别是在高强度噪声水平下，去噪优势更为突出。
