<p>Due to the limitations of X-ray digital imaging in handling workpieces with a large thickness variation range, Moreover, digital radiography (DR) itself is prone to noise, uneven grayscale distribution, and it is difficult for the human eye to obtain useful information from it. In this study, we propose an enhanced visual sensitivity fusion method based on the non-subsampled shearlet transform (NSST), leveraging the inherent characteristics of the human visual system. Consequently, the fused DR image exhibits heightened sensitivity to human perception and enhances discernibility. This facilitates easier observation of distinctive features in DR images and enables more effective extraction of crucial information. The NSST is used to decompose the source image into high- and low-frequency coefficients through multi-scale decomposition. The proposed fusion rule, which enhances visual sensitivity, is applied to combine the high-frequency coefficients. The fused image is then reconstructed using the inverse NSST transform by incorporating both high- and low-frequency coefficients. Experimental results demonstrate that our method outperforms traditional fusion methods and other fusion rules in terms of objective evaluation indicators, such as IE, PSNR, and SSIM. Moreover, our method achieves superior fusion results for workpieces with large thickness variations. Compared to deep learning methods, our approach exhibits wider adaptability. Compared with deep learning methods, it is not limited by memory. The fusion rules also enhance details, enabling clear representation of complex internal structures and defects within workpieces. Notably, utilizing GPU parallel computing significantly reduces the algorithm’s running time.</p>