LG-DBNet: Local and Global Dual-Branch Network for SAR Image Denoising

Synthetic aperture radar (SAR) tends to be seriously affected by speckle noise due to its inherent imaging characteristics, which brings great challenges to the high-level visualization task of SAR images. Therefore, speckle suppression plays a crucial role in remote sensing image processing. Attention-based SAR image denoising algorithms frequently struggle to capture rich feature information and face challenges in balancing the trade-off between denoising and preserving texture details. To solve the above problems, this paper constructs a local and global dual-branch network (LG-DBNet) for SAR image denoising. This network can effectively suppress speckle noise while fully retaining the detail information of the original image. Firstly, the shallow features are extracted through simple convolution. Then, a dual-branch structure constructed using different attention modules is used to extract deep features from SAR images. Specifically, one branch performs local deep feature extraction of an image through a hybrid attention module built by a convolutional neural network (CNN), while the other branch utilizes a superposition of self-attention mechanisms for global deep feature extraction of the image. Finally, the final denoised image is generated through global residual learning. LG-DBNet can effectively extract the local and global image information through the dual-branch structure, and further focus on the noise information, which can better retain the texture information of the image while effectively denoising. The experimental results show that compared with the state-of-the-art SAR image denoising algorithms, the proposed algorithm not only improves on various objective indexes, but also shows great advantages in the visual effect after denoising.