DA-PhysDiff: A Dual-Aware Physics-Informed Diffusion Model for SAR Image Despeckling

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Abstract
Deep learning-based Synthetic Aperture Radar (SAR) despeckling faces two key challenges: physical model mismatch, where simplified noise assumptions fail to capture speckle’s statistics, and architectural generality, where standard networks struggle to separate noise from texture. To address this, we propose DA-PhysDiff, a dual-aware physics-informed diffusion model for SAR image despeckling. The proposed LogGamma-based stochastic differential equation models multiplicative speckle more accurately, while the dual-aware network composed of a Statistics-Conditioned Gamma Attention (SCGA) and a Frequency-Refined Adaptive Wavelet Convolution (FRA-WTConv) enhances both distribution and frequency awareness. Experiments on HRSID, MiniSAR, and synthetic benchmarks demonstrate that DA-PhysDiff achieves up to +2.5 dB PSNR and +4.8% EPD-ROA improvement over recent diffusion models, while reducing parameters by 19% compared with SAR-DDPM and maintaining real-time inference.
Funding Information
  • National Natural Science Foundation of China (61503005)
  • Humanities and Social Sciences of the Ministry of Education in China (22YJAZH002)

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