SAR Image Despeckling Using a Convolutional Neural Network

Puyang Wang, He Zhang, Vishal M. Patel

Abstract: Synthetic aperture radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep-learning-based approach called, image despeckling convolutional neural network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit activation function and a componentwise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and total variation loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.