基于轻量密集神经网络的医学图像超分辨率重建算法

医学图像的清晰与否直接影响临床诊断。由于成像设备与环境因素的限制，往往不能直接获得高分辨率的图像，且大多数智能终端的硬件并不适合运行大规模深度神经网络模型，因此提出一种拥有较少的层和参数的轻量密集神经网络模型。首先，网络中使用密集块和跳层结构进行全局和局部图像特征学习，并将更多特征信息传入激活函数，从而使网络中浅层低级的图像特征更容易传播到高层，由此提高医学图像超分辨率重建的质量；然后，采用分阶段方法训练网络，并以双任务损失加强网络学习中的监督指导，从而解决高倍图像超分辨率重建导致的网络训练难度增加的问题。实验结果表明，与最近邻（NN）插值、双线性插值、双立方插值、基于卷积神经网络（CNN）的算法以及基于残差神经网络的算法相比，所提模型能更好地重建出医学图像的纹理细节，获得更高的峰值信噪比（PSNR）和结构相似性（SSIM），在训练速度和硬件消耗方面均取得了良好的效果，具有较高的实用价值。

Super-resolution reconstruction algorithm of medical image based on lightweight dense neural network

The clarity of medical images directly affects the clinical diagnosis. Due to the limitations of imaging equipment and environmental factors， it is often impossible to directly obtain high-resolution images， and the hardware of most smart terminals is not suitable for running large-scale deep neural network models. Therefore， a lightweight dense neural network model with fewer layers and parameters was proposed. First of all， dense block and skip layer structure were used in the network for global and local image feature learning， and more feature information was introduced into the activation function， so that the shallow low-level image features in the network were able to be propagated to the high-layers more easily， thereby improving the super-resolution reconstruction quality of medical images. Then， the multi-stage method was adopted to train the network and the dual-task loss was used to strengthen the supervision and guidance in network learning， which solved the problem of difficulty increase in network training caused by highly magnified image super-resolution reconstruction. Compared with Nearest Neighbor （NN）， bilinear interpolation， bicubic interpolation， Convolutional Neural Network （CNN） based algorithm and the residual neural network based algorithm， the proposed model is of high practical value on better reconstructing the texture details of medical images， achieving higher Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM）， as well as achieving good result in both training speed and hardware consumption.