HTCNet: Hybrid Transformer-CNN for SAR Image Denoising

Abstract

Synthetic aperture radar (SAR) is extensively utilized in diverse fields, including military defense and resource exploration, due to its all-day, all-weather characteristics. However, the extraction of information from SAR images is severely affected by speckle noise, making denoising crucial. This article proposes a hybrid transformer-convolutional neural networks (CNNs) network, a hybrid denoising network that combines transformer and CNN. The three core designs of the network ensure its suitability for SAR image denoising: 1) The network integrates a transformer-based encoder with a CNN-based decoder, capturing both local and global dependencies inherent in SAR images, thereby enhancing the effectiveness of noise removal. 2) Patch embedding blocks enhance the convolutional neural network's perception of features at different scales. 3) Depthwise separable convolutions are fused into the Transformer block to further improve the network's ability to capture spatial information while reducing computational complexity. The proposed algorithm demonstrates excellent denoising performance in both simulated and real SAR images, as evidenced by experimental results. Compared to other denoising algorithms, this method efficiently removes speckle noise while preserving the texture information within the images.

Keywords

• Convolutional neural networks (CNNs)
• image denoising
• synthetic aperture radar
• transformer