SAR Image Despeckling Using a Convolutional Neural Network

By Puyang Wang, He Zhang, Vishal M. Patel. IEEE Signal Processing Letters, 2017

377 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/6152d44e73e83c7c6f6b789f2b4b6c3fe9b91663

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1. Xiaoxiang Zhu
2. Richard Bamler
3. Lichao Mou
4. Yuanyuan Wang
5. Feng Xu
6. Mohsin Ali
7. Sina Montazeri
8. Yilei Shi

Citations in the corpus

• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “[9] proposed a CNN, called ID-CNN, for image despeckling, which can directly learn denoised images via a component-wise division-residual layer with skip connections.”