Scattering Guided Image Despeckling: A Score-Based Diffusion Approach in Frequency Domain

Sihao Dong, Xiayang Xiao, Mingjun Pan

Abstract: When it comes to deep-learning-based methods for despeckling synthetic aperture radar (SAR) images, maintaining spatial structure, ensuring process stability, and achieving fast inference are still major challenges. To address these issues, a novel method named scattering guided image despeckling (SGID), which is based on the wavelet—transform—conditioned diffusion model for SAR despeckling, has been proposed. First, the core component of this approach is the adaptive scattering extraction module (ASEM), which is designed to preserve the spatial structure of images effectively. It achieves the preservation of spatial structure by leveraging SAR imaging principles to adaptively extract scattering points, thereby retaining critical spatial information. Second, considering that speckle noise in SAR images is mainly concentrated in the high-frequency region, this study has designed a high-frequency despeckling module (HFDM). This module adopts a direction-aware adaptive weight allocation strategy, aiming to precisely regulate the weight allocation of high-frequency components in all the directions during the processing, so as to maximize the coherence and consistency of the high-frequency detail structures in the images. Finally, the entire processing workflow is conducted in the frequency domain, which contributes significantly to the improvement of inference speed. Experimental results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in both spatial structure preservation and despeckling quality. Besides, its inference speed is 0.19 s, which is 67 times faster than that of comparable diffusion-model-based methods. The code for this study will be publicly available at https://github.com/SihaoDong/SGID.

External IDs:dblp:journals/lgrs/DongXP25