High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent analysis. However, the acquisition of HR medical images is easily affected by hardware devices. As an effective and trusted alternative method, the super-resolution (SR) technology is introduced to improve the image resolution. Compared with traditional SR methods, the deep learning-based SR methods can obtain more clear and trusted HR images. In this paper, we propose a trusted deep convolutional neural network-based SR method named feedback adaptive weighted dense network (FAWDN) for HR medical image reconstruction. Specifically, the proposed FAWDN can transmit the information of the output image to the low-level features by a feedback connection. To explore advanced feature representation and reduce the feature redundancy in dense blocks, an adaptive weighted dense block (AWDB) is introduced to adaptively select the informative features. Experimental results demonstrate that our FAWDN outperforms the state-of-the-art image SR methods and can obtain more clear and trusted medical images than comparative methods.

高分辨率 (HR) 医学图像在临床诊断和后续分析中是首选。但是，HR 医学图像的获取容易受到硬件设备的影响。作为一种有效且值得信赖的替代方法，引入了超分辨率（SR）技术来提高图像分辨率。与传统的 SR 方法相比，基于深度学习的 SR 方法可以获得更清晰和可信的 HR 图像。在本文中，我们提出了一种基于可信深度卷积神经网络的 SR 方法，称为反馈自适应加权密集网络（FAWDN），用于 HR 医学图像重建。具体来说，所提出的 FAWDN 可以通过反馈连接将输出图像的信息传递给低级特征。为了探索高级特征表示并减少密集块中的特征冗余，引入了自适应加权密集块（AWDB）来自适应地选择信息特征。实验结果表明，我们的 FAWDN 优于最先进的图像 SR 方法，并且可以比比较方法获得更清晰、更可信的医学图像。