Abstract: In sparse-view computed tomography (CT), incomplete projection data often lead to artifacts, edge blurring, and structural distortions in the reconstructed images. To address this challenge, this study proposes a diffusion bridge model based on the Ornstein–Uhlenbeck (OU) process for the conditional completion of missing projection-domain data to enable high-quality sparse-view reconstruction. The proposed method leverages the mean-reverting property of the OU process to establish a physically consistent stochastic diffusion mechanism. The diffusion bridge constraint anchors the sampling trajectory between the reconstruction prior and sparse projections, thereby achieving high-fidelity restoration of the missing data. Furthermore, a multiscale feature fusion module in the wavelet domain is incorporated into the network architecture to effectively integrate low-frequency structural information with high-frequency texture details, thereby enhancing the capability of the model to recover fine image features. Experimental results demonstrate that under the same sparse-view conditions, the proposed method outperforms existing state-of-the-art approaches in terms of both visual quality and quantitative metrics, confirming its effectiveness for sparse-view CT reconstruction tasks.