针对医学图像超分辨率重建过程中高频信息缺失导致的模糊问题,提出了一种基于残差通道注意力网络的医学图像超分辨率方法。提出的方法在残差网络的基本单元上去除了批规范化层以稳定训练;去掉缩放层、添加通道注意力块,使神经网络更加关注含有丰富高频信息的通道;使用亚像素卷积层进行上采样操作得到最终输出的高分辨率图像。实验结果表明,提出的方法相比主流的图像超分辨率方法在客观评价指标如峰值信噪比和结构相似性上有显著提升,得到的医学图像纹理细节丰富,视觉体验较好。

To resolve the fuzzy problem caused by the lack of high-frequency information in the super-resolution reconstruction of medical images, this study proposes a medical-image super-resolution reconstruction method based on a residual channel attention network. The proposed method removes the batch normalization layer from the basic unit of the residual network (ResNet) to stabilize its training. Furthermore, it removes the scaling layer and adds a channel-attention block that focuses the ResNet on channels with abundant high-frequency details. The feature maps are subsampled using a sub-pixel convolution layer,obtaining the final high-resolution images. Experimental results show that the proposed method significantly improves objective evaluation indexes such as the peak signal-to-noise ratio and structural similarity index compared with mainstream image super-resolution methods. The obtained medical images are sufficiently detailed with high visual quality.