In the field of digital radiography (DR) testing, high-resolution DR images facilitate better detection of internal defects and structural information in workpieces. However, the increase of resolution typically leads to higher equipment costs and reduced detection efficiency, while existing reconstruction methods suffer from suboptimal results and low efficiency, failing to meet the testing demands of industrial applications. To address these limitations, this study proposes a latent space diffusion model for DR image super-resolution enhancement. The algorithm employs a two-stage framework: in the first stage, a lightweight autoencoder constructed with a fully convolutional neural network maps input images to low-dimensional potential space; in the second stage, the denoising diffusion implicit model is utilized for detail reconstruction in the latent space, conditioned on the latent feature vectors. To compensate for lost high-frequency information in the latent space, a frequency-domain information-guided cross-attention structure is designed within the diffusion model's denoising network, which incorporates four frequency components obtained through wavelet transform as additional conditional inputs to enhance global modeling capabilities. Finally, the autoencoder and diffusion model are trained separately using optimized perceptual loss functions. Experimental results on the self-constructed dataset demonstrate that the proposed algorithm achieves significant advantages in both reconstruction quality and processing speed compared to various mainstream methods, confirming its effectiveness for DR image super-resolution enhancement.