Acoustic-resolution photoacoustic microscopy (AR-PAM) suffers from degraded lateral resolution due to acoustic diffraction. Here, a resolution enhancement strategy for AR-PAM via a mean-reverting diffusion model was proposed to achieve the transition from acoustic resolution to optical resolution. By modeling the degradation process from high-resolution image to low-resolution AR-PAM image with stable Gaussian noise (i.e., mean state), a mean-reverting diffusion model is trained to learn prior information of the data distribution. Then the learned prior is employed to generate a high-resolution image from the AR-PAM image by iteratively sampling the noisy state. The performance of the proposed method was validated utilizing the simulated and in vivo experimental data under varying lateral resolutions and noise levels. The results show that an over 3.6-fold enhancement in lateral resolution was achieved. The image quality can be effectively improved, with a notable enhancement of ∼66% in PSNR and ∼480% in SSIM for in vivo data.