Generative adversarial network-based restoration of speckled SAR images

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 Generative Adversarial Network (ID-GAN), for automatically removing speckle from the input noisy images. In particular, ID-GAN is trained in an end-to-end fashion using a combination of Euclidean loss, Perceptual loss and Adversarial 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.