Frequency Domain Mamba-Based Diffusion Model for Power Image Super-Resolution

To address the issues of low resolution and missing edge details in current power imagery, this paper proposes a diffusion-based super-resolution method. By applying the iterative denoising process of diffusion models, the proposed method learns feature distributions from training images, guiding the recovery of edge details. Unlike conventional diffusion methods, which directly use low-resolution images to guide noise towards high-resolution images, we propose a frequency-domain Mamba module. This module separates the image into high-frequency and low-frequency components through wavelet transform. The low-frequency components are processed using a Mamba network to extract features, while multi-scale convolution is employed to restore high-frequency details. The initial prediction generated by the frequency-domain Mamba module guides the diffusion model to restore high-resolution images. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches on power images.