Frequency-enhanced diffusion prior network for joint SAR image despeckling and super-resolution
TL;DR
A novel two-stage cascaded deep learning framework for high-quality SAR image reconstruction that sequentially integrates a window-based Transformer denoising subnet and a diffusion-driven super-resolution subnet via a learnable projection matrix provides an effective lightweight solution for high-precision SAR reconstruction applicable to military reconnaissance, geological exploration, and disaster monitoring.
Abstract
Synthetic aperture radar (SAR) image enhancement faces inherent difficulties in simultaneous speckle suppression and structural detail preservation, severely limiting the performance of conventional methods in high-precision remote sensing tasks. This paper proposes a novel two-stage cascaded deep learning framework for high-quality SAR image reconstruction, which sequentially integrates a window-based Transformer denoising subnet and a diffusion-driven super-resolution subnet via a learnable projection matrix. To address the coupled degradation of speckle noise, imaging blurring, and structural loss, four dedicated designs are adopted, including a projection-coupled serial architecture for joint optimization and lightweight parameter deployment (24.3% parameter reduction), a Frequency Enhancement Module for high-frequency detail recovery, an Adaptive Noise Scheduler for robust diffusion adjustment under complex textures and low-contrast conditions, and Meta Residual Connections for stable deep feature propagation. Quantitative experiments on the FAIR-CSAR-V1.0 ×4 super-resolution benchmark demonstrate that the proposed method achieves state-of-the-art performance with 19.26 dB PSNR, 0.3502 SSIM, 2.23 ENL, and 2.26 RadRes. Our method outperforms the baseline model by 5.71% in PSNR and 30.4% in SSIM, and surpasses existing SOTA methods by 31.9%, 108.2%, 105.6%, and 27.3% in four metrics, respectively, with prominent gains mainly obtained in challenging noisy and low-contrast SAR regions. Ablation studies validate the efficacy of each component. The proposed framework provides an effective lightweight solution for high-precision SAR reconstruction applicable to military reconnaissance, geological exploration, and disaster monitoring.