X-ray image super-resolution reconstruction based on a multiple distillation feedback network

Abstract

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.