The super-resolution reconstruction of X-ray images is one of the hot issues in the field of medical imaging. Due to the limitations of X-ray machines, the acquired images often have some problems, such as blurred details, unclear edges and low contrast, which seriously affect doctors’ interpretations of X-ray images. In view of the above problems, an X-ray image super-resolution reconstruction method based on a multiple distillation feedback network is proposed. In the feature extraction stage, the shallow features of X-ray images are extracted through 3 × 3 and 1 × 1 convolutional layers, and a multiple distillation feedback module is designed, which iteratively upsamples and downsamples to fully extract the texture details of X-ray images. Subpixel convolution is used to improve the resolution and the residual convolution is used to predict the corresponding residual image. In the image reconstruction stage, the residual image is fused with the transposed convolution upsampled image, and the Laplacian pyramid structure is used to progressively reconstruct high-resolution X-ray images. The experimental results show that the proposed method can suppress noise and reduce artifacts. Its peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and information fidelity criteria (IFC) were all higher than those of the comparison methods, and its subjective visual was better.

X射线图像的超分辨率重建是医学成像领域的热点问题之一。由于X射线机的局限性，所采集的图像常常存在一些问题，例如细节模糊，边缘不清晰和对比度低，严重影响了医生对X射线图像的解释。针对上述问题，提出了一种基于多重蒸馏反馈网络的X射线图像超分辨率重建方法。在特征提取阶段，通过3×3和1×1卷积层提取X射线图像的浅层特征，并设计了多重蒸馏反馈模块，该模块反复上采样和下采样以完全提取X-射线的纹理细节。射线图像。亚像素卷积用于提高分辨率，残差卷积用于预测相应的残差图像。在图像重建阶段，将残差图像与转置的卷积上采样图像融合，并使用拉普拉斯金字塔结构逐步重建高分辨率X射线图像。实验结果表明，该方法可以抑制噪声，减少伪影。其峰值信噪比（PSNR），结构相似度（SSIM）和信息保真度标准（IFC）均高于比较方法，其主观视觉效果更好。拉普拉斯金字塔结构用于逐步重建高分辨率X射线图像。实验结果表明，该方法可以抑制噪声，减少伪影。其峰值信噪比（PSNR），结构相似度（SSIM）和信息保真度标准（IFC）均高于比较方法，其主观视觉效果更好。拉普拉斯金字塔结构用于逐步重建高分辨率X射线图像。实验结果表明，该方法可以抑制噪声，减少伪影。其峰值信噪比（PSNR），结构相似度（SSIM）和信息保真度标准（IFC）均高于比较方法，其主观视觉效果更好。