In industrial microfocus cone-beam CT, when the number of scanning angles is sufficiently large, the images reconstructed using analytical algorithms exhibit high quality. Conversely, when the number of scanning angles is insufficient, the reconstructed images exhibit severe strip-like artifacts during sparse-angle scanning. To address strip-like artifacts caused by sparse-view scanning, deep-learning techniques are applied to assist in reconstruction. This approach reduces the number of scans and thus shortens the scanning time, while ensuring that the reconstruction quality is maintained. Specifically, the input image is first subjected to a discrete wavelet transform. Subsequently, a denoising diffusion probabilistic model (DDPM) is employed to learn the corresponding coefficients for the low-frequency components of the original image and the sparse-view reconstructed image after wavelet transformation. For the high-frequency components, a high-frequency recovery module based on a depthwise separable convolution is used for training. Finally, the image is synthesized via inverse wavelet transform. Experimental results show that the wavelet-based DDPM outperforms several other reconstruction methods on a custom-developed ball grid array dataset. By reducing the number of scans from 1024 to 180, the scanning time is 17.58% of the original. Meanwhile, the method effectively suppresses artifacts, preserves high-frequency details, and achieves superior performance in both qualitative and quantitative evaluations compared with other approaches.