In micro focus cone beam CT imaging, a large scanning angle is usually required to obtain sufficient data. If the scanning angle is reduced, it may result in artifacts in the reconstructed image. To address the problems of stripe artifacts and image blurring owing to sparse scanning, deep learning is used to fill in information gaps caused by sparse scanning. A paired dataset is constructed using full-scan and sparse-scan reconstructed images. A dual-domain CT sparse-angle reconstruction model is proposed that combines a wavelet transform denoising diffusion probability model and a generative adversarial network (GAN)-based wavelet diffusion refinement generation (WDRG) model. This model separates the domain transforms in CT reconstruction for processing. First, a low-frequency diffusion module and a high-frequency refinement module are introduced in the sine domain. Thereafter, the sine map is fully reconstructed in the reconstruction domain. Finally, the optimized GAN model is applied in the image domain to generate the final CT-reconstructed image. Simulation experiments and actual results demonstrate that the proposed WDRG model achieves high-quality CT image reconstruction in sparse-angle scanning while considerably reducing scan time.