Research on image restoration of [formula omitted] porous foam ceramics media using Transformer architecture

Abstract

Pore structure images of foam ceramics play a crucial role in the field of refractory materials science and engineering, facilitating the observation and analysis of the material’s pore structure and unique characteristics. However, traditional image restoration techniques have limitations in handling background noise, complex texture restoration, deformation, and dislocation correction, as well as automation and intelligence aspects in restoring material pore structure images. The latest Transformer architecture has shown significant potential in image processing, particularly in image restoration research tasks. Nevertheless, its application in restoring pore structure images of materials remains unexplored. This paper aims to address this gap by focusing on the image restoration of foam ceramic pore structure images using a Transformer-based algorithm for super-resolution (SR) processing. Experimental comparisons with state-of-the-art methods demonstrate a remarkable improvement in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), underscoring the effectiveness of the proposed approach.