Industrial digital radiographic image denoising based on improved KBNet.

Abstract

 Industrial digital radiography (DR) images are essential for industrial inspections, but they often suffer from strong scatter, cross-talk, electronic noise, and other factors that affect image quality. The presence of non-zero mean noise and neighborhood correlation loss in 1D array scanning poses significant challenges for denoising. To enhance the denoising process of industrial DR images and address the issues of low resolution and noise, we propose an improved KBNet (iKBNet) that incorporates lightweight modifications and introduces novel elements to the original KBNet. The iKBNet introduces the Convolutional Block Attention Module (CBAM) to reduce the network's parameter count. Additionally, it utilizes the Structural Similarity Index (SSIM) loss as part of a composite loss function to improve denoising performance. The proposed method demonstrates superior denoising results, with image restoration quality metrics that surpass those of commonly used methods such as BM3D, ResNet, DnCNN, and the original KBNet. In practical applications with low-resolution transmission images, the iKBNet has produced satisfactory outputs. The results indicate that the iKBNet not only minimizes computational cost and enhances processing speed but also achieves better denoising results. This suggests the potential of iKBNet for processing noisy digital radiographic images in industrial settings. The iKBNet shows promise in improving the quality of industrial DR images affected by noise, offering a viable solution for industrial image processing needs.