Diffusion super-resolution of SAR images integrating wavelet guidance and structural enhancement

Influenced by scattering characteristics and imaging geometry, single-polarization SAR remote sensing images suffer from severe speckle noise and significant degradation, leading to texture loss and structural distortion in SAR image super-resolution tasks. To address this issue, this paper proposes a diffusion model-based super-resolution method that integrates frequency-domain processing and structural perception. The method adopts a latent diffusion model as its backbone and introduces a wavelet-guided module and a direction-aware enhancement module to improve performance. The wavelet-guided module performs multi-scale modulation of high-frequency sub-bands through wavelet decomposition and spatially adaptive normalization, thereby dynamically enhancing texture representation and high-frequency reconstruction capability according to the diffusion timestep. The direction-aware enhancement module incorporates multiple adaptive convolutional kernels, embedded with channel attention into the deep residual structures of the encoder to increase the sensitivity of compressed feature maps to structural information. In experiments, a realistic degradation model combining speckle noise and blur kernels is established to closely approximate the actual imaging pipeline. Results demonstrate that the proposed method significantly outperforms existing frameworks across multiple datasets, achieving an average improvement of 8.12% over the best baseline, verifying its effectiveness and advancement in SAR image super-resolution tasks.