In view of the problem that the existing reconstruction methods have no ideal effect due to the texture complexity during the super-resolution reconstruction of rock thin slice images, this study proposes a super-resolution denoising diffusion probability model of rock slice (rsDDPMSR). To solve the problem that the traditional upsampling methods tend to cause artifacts and insufficient utilization of prior information of low-resolution images, it puts forward a layered feature enhancement network (LFE-Net). Meanwhile, a dual-path network is employed to conduct layered feature enhancement on the high-frequency and low-frequency components decomposed by the stationary wavelet transform. The low-resolution features enhanced by LFE-Net are combined with the feature channels of the target high-resolution noisy image as the conditional input of the diffusion model to guide the generation direction of the diffusion model and provide rich prior information. Based on U-Net, a double-encoder multi-scale noise prediction network (ACA-U-Net) is designed to effectively process multi-scale information of rock slices, and a time-aware adaptive cross-attention mechanism is introduced into the skip connections to match the feature distribution changes in different denoising stages of the diffusion model to enhance the model’s attention to key areas and effectively improve image reconstruction details. The experimental results show that rsDDPMSR has a better reconstruction effect at 2×, 4×, and 8× magnification than other mainstream reconstruction methods, such as CAMixerSR, SDFlow, IDM, and SR3.