Restoration of Material Pore Structure Image Using Transformer Architecture

The latest Transformer architecture has shown significant progress in the field of image restoration. However, research on the application of the Transformer architecture in material scanning electron microscopy (SEM) image restoration is still lacking. Material SEM images are typically influenced by factors such as noise, exposure, and distortion, resulting in low-resolution (LR) image and clarity. The self-attention mechanism and global information interaction capabilities of the Transformer architecture make it perform well in image restoration tasks. This paper uses a Transformer-based algorithm to study the restoration of sandstone SEM images. The experimental results demonstrate that the Transformer-based algorithm is capable of restore the details and edge information of the sandstone SEM images, improve their resolution and clarity, and enhance the overall image quality. Moreover, the algorithm is able to learn the distribution of the images. The PSNR and SSIM of this method reach a maximum of 34.59dB and 0.8893, which are improved compared to other SOTA algorithms.