There are two key requirements for medical lesion image super-resolution reconstruction in intelligent healthcare systems: clarity and reality. Because only clear and real super-resolution medical images can effectively help doctors observe the lesions of the disease. The existing super-resolution methods based on pixel space optimization often lack high-frequency details which result in blurred detail features and unclear visual perception. Also, the super-resolution methods based on feature space optimization usually have artifacts or structural deformation in the generated image. This paper proposes a novel pyramidal feature multi-distillation network for super-resolution reconstruction of medical images in intelligent healthcare systems. Firstly, we design a multi-distillation block that combines pyramidal convolution and shallow residual block. Secondly, we construct a two-branch super-resolution network to optimize the visual perception quality of the super-resolution branch by fusing the information of the gradient map branch. Finally, we combine contextual loss and L1 loss in the gradient map branch to optimize the quality of visual perception and design the information entropy contrast-aware channel attention to give different weights to the feature map. Besides, we use an arbitrary scale upsampler to achieve super-resolution reconstruction at any scale factor. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance compared to other methods in this work.

智能医疗系统中医学病灶图像超分辨率重建有两个关键要求：清晰度和真实性。因为只有清晰真实的超分辨率医学图像才能有效帮助医生观察疾病的病变情况。现有的基于像素空间优化的超分辨率方法往往缺乏高频细节，导致细节特征模糊和视觉感知不清晰。此外，基于特征空间优化的超分辨率方法通常在生成的图像中存在伪影或结构变形。本文提出了一种新颖的金字塔特征多重蒸馏网络，用于智能医疗系统中医学图像的超分辨率重建。首先，我们设计了一个结合金字塔卷积和浅残差块的多重蒸馏块。其次，我们构建了一个双分支超分辨率网络，通过融合梯度图分支的信息来优化超分辨率分支的视觉感知质量。最后，我们在梯度图分支中结合上下文损失和 L1 损失来优化视觉感知质量，并设计信息熵对比度感知通道注意力，为特征图赋予不同的权重。此外，我们使用任意比例上采样器来实现任意比例因子的超分辨率重建。实验结果表明，与本工作中的其他方法相比，所提出的超分辨率重建方法取得了优越的性能。