  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 (ReLU) activation function and a component-wise 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 (TV) 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.
