the code of SFSG-Diff
Abstract
We propose a Spatial-Frequency Strongly-Sparse Guided Diffusion Model (SFSG-Diff) aimed at achieving more robust and efficient S2O image translation. To effectively suppress SAR speckle noise and enhance structural representation, a Multi-scale Spatial-Frequency Denoising Encoder (MDE) is designed to extract robust conditional features from the input SAR image. To further improve conditional guidance efficiency, we introduce a lightweight Strong-Sparse Semantic Fusion (SIF) pattern. This sparse guidance mechanism not only reduces computational overhead but also improves cross-modal semantic alignment by emphasizing the most relevant structural cues. Furthermore, to enhance training stability and generation quality, we adopt a two-stage training strategy, leveraging a joint loss, including a simplified mean squared error loss, perceptual loss, focus frequency loss, and adversarial loss.
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