Learning synthetic aperture radar image despeckling without clean data

Abstract. Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and make interpretation more difficult. Existing SAR image despeckling convolutional neural networks require quantities of noisy–clean image pairs. However, obtaining clean SAR images is very difficult. Because continuous convolution and pooling operations result in losing many informational details while extracting the deep features of the SAR image, the quality of recovered clean images becomes worse. Therefore, we propose a despeckling network called multiscale dilated residual U-Net (MDRU-Net). The MDRU-Net can be trained directly using noisy–noisy image pairs without clean data. To protect more SAR image details, we design five multiscale dilated convolution modules that extract and fuse multiscale features. Considering that the deep and shallow features are very distinct in fusion, we design different dilation residual skip connections, which make features at the same level have the same convolution operations. Afterward, we present an effective L_hybrid loss function that can effectively improve the network stability and suppress artifacts in the predicted clean SAR image. Compared with the state-of-the-art despeckling algorithms, the proposed MDRU-Net achieves a significant improvement in several key metrics.