Contrastive learning for real SAR image despeckling

Abstract

The use of synthetic aperture radar (SAR) has greatly improved our ability to capture high-resolution terrestrial images under various weather conditions. However, SAR imagery is affected by speckle noise, which distorts image details and hampers subsequent applications. Recent forays into supervised deep learning-based denoising methods, like MRDDANet and SAR-CAM, offer a promising avenue for SAR despeckling. However, they are impeded by the domain gaps between synthetic data and realistic SAR images. To tackle this problem, we introduce a self-supervised speckle-aware network to utilize the limited near-real datasets and unlimited synthetic datasets simultaneously, which boosts the performance of the downstream despeckling module by teaching the module to discriminate the domain gap of different datasets in the embedding space. Specifically, based on contrastive learning, the speckle-aware network first characterizes the discriminative representations of spatial-correlated speckle noise in different images across diverse datasets, which provides priors of versatile speckles and image characteristics. Then, the representations are effectively modulated into a subsequent multi-scale despeckling network to generate authentic despeckled images. In this way, the despeckling module can reconstruct reliable SAR image characteristics by learning from near-real datasets, while the generalization performance is guaranteed by learning abundant patterns from synthetic datasets simultaneously. Additionally, a novel excitation aggregation pooling module is inserted into the despeckling network to enhance the network further, which utilizes features from different levels of scales and better preserves spatial details around strong scatters in real SAR images. Extensive experiments across real SAR datasets from Sentinel-1, Capella-X, and TerraSAR-X satellites are carried out to verify the effectiveness of the proposed method over other state-of-the-art methods. Specifically, the proposed method achieves the best PSNR and SSIM values evaluated on the near-real Sentinel-1 dataset, with gains of 0.22 dB in PSNR compared to MRDDANet, and improvements of 1.3% in SSIM over SAR-CAM. The code is available at https://github.com/YangtianFang2002/CL-SAR-Despeckling.