LAUSR

The coherent imaging method of synthetic aperture radar (SAR) brings SAR images with strong and randomly distributed speckle, which causes great interference to subsequent applications. To deal with the affected images, we propose a multiconnection network incorporating wavelet features (MCN-WF) to despeckle the images and then evaluate the results. On the one hand, simplified dense connections in and among Dense Blocks (DBs) utilize the features extracted from the network at different scales to produce despeckled images with more details. On the other hand, performing feature pre-extraction on images by wavelet transform can not only indirectly control the convergence direction of the network by modifying the loss function but also reduce the size of the feature maps to accelerate the speed of the network processing. The experimental results show that the new method has a better performance in terms of despecking, image texture structure preservation, and processing efficiency.