Details

Autor(en) / Beteiligte

• Puyang Wang
• He Zhang
• Patel, Vishal M.

Titel

SAR Image Despeckling Using a Convolutional Neural Network

Ist Teil von

• IEEE signal processing letters, 2017-12, Vol.24 (12), p.1763-1767

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Link zum Volltext

• Volltext

Quelle

IEEE Xplore (IEEE/IET Electronic Library - IEL)

Beschreibungen/Notizen

• 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.