An explainable super-resolution visual method for micro-crack image detection

Abstract

To improve the performance of crack image detection in the construction industry, this paper designs a gradient-guided micro-crack image super-resolution (SR) visual method. Firstly, to solve the problem of low resolution (LR) and smooth grayscale differences in the images, an interpretable gradient-guided image SR model is developed to achieve high-fidelity SR reconstruction of LR images. Then, to address the large amount of interference noise in the background, a micro-crack pixel-level selection module is proposed based on the SR model, which achieved high-fidelity reconstruction of the micro-crack region while reducing the impact of interference noise to a certain extent. Finally, this paper validates and analyzes the performance of the proposed methods through a real crack image dataset, showing the effectiveness of the proposed methods.