ANED-Net: Adaptive Noise Estimation and Despeckling Network for SAR Image

Abstract

Synthetic aperture radar (SAR) images are often affected by a type of multiplicative noise known as “speckle” due to their active imaging characteristics. This property complicates the processing and interpretation of SAR images. While deep learning techniques have demonstrated success in despeckling, many models are tailored to specific noise levels. This specificity can limit a model's ability to generalize to real SAR images with varying noise levels, potentially leading to oversmoothing or overfocusing on specific details. To address these challenges, we present the Adaptive Noise Estimation and Despeckling Network (ANED-Net). This network consists of a noise-level estimation phase and a noise-level-guided nonblind denoising phase. During the nonblind denoising phase, we develop a noise-feature-guided denoising network. This network integrates a hierarchical encoder–decoder denoising module based on the Transformer block (T-unet) and a denoising enhancement control block. Together, they skillfully capture both local and global dependencies inherent in SAR images, facilitating effective noise removal. Furthermore, we also introduce a deep-attention mechanism to counteract the attentional collapse observed when the Transformer is extended in depth, enhancing the network's feature extraction capability and strengthening the model's denoising performance. Extensive tests on synthetic and real images show that ANED-Net is robust to different noise scenarios. It effectively mitigates speckle noise even at unspecified levels and outperforms many established methods.

Keywords

• Deep learning
• noise-level estimation
• speckle noise suppression
• synthetic aperture radar (SAR)