Abstract :
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.
