High-precision digital terrain models (DTMs) are important for Mars explorations and research, providing indispensable spatial information for landing site assessment, rover path planning, and surface environment analysis. However, challenges such as high-resolution stereo data scarcity and complex atmospheric conditions on the Martian surface result in traditional terrain reconstruction methods suffer from limitations in accuracy, coverage and resolution. To enhance the model’s ability to recover fine-grained topography, we present a diffusion-based monocular terrain reconstruction method, which progressively recovers Martian terrains from single-view high-resolution optical images. We employed a multi-scale U-Net denoising network with attention mechanisms and introduced an additional end-to-end depth constraint. To improve terrain reconstruction efficiency, we implemented a diffusion model in the latent space and adopted a skipping sampling mechanism. We employed the proposed method to reconstruct terrain in different regions. Experimental results demonstrate that the reconstructed terrain achieves an accuracy of 2 m. Furthermore, compared to photogrammetric terrain, the shaded relief generated by our method exhibits greater similarity to the input imagery.