MFAENet: A Multiscale Feature Adaptive Enhancement Network for SAR Image Despeckling

The existence of speckles in synthetic aperture radar (SAR) images affects its subsequent application in computer vision tasks, so the research of speckle suppression plays a very important role. Convolutional neural networks based speckle suppression algorithms cannot reach a good balance between despeckling effect and structure detail preservation. Considering these issues, a multiscale feature adaptive enhance network for suppressing speckle is proposed. Specifically, an encoder-decoder architecture embedded with multiscale operations is constructed to capture rich contextual information and remove speckles from coarse to fine. Then, deformable convolution is introduced to flexibly adapt changes in ground objects' complex and diverse image features. Also, the constructed feature adaptive mixup module mitigates shallow feature degradation in deep networks by establishing connections between shallow image texture features and deep image semantic features with learnable weights. Experiments on synthetic and real SAR images show that the proposed method produces advanced results regarding visual quality and objective metrics.