SAR image despeckling through convolutional neural networks

Abstract

In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.Index Terms-SAR, speckle, multiplicative noise, convolutional neural networks.

Learning a Dilated Residual Network for SAR Image Despeckling

“…We used the Caffe [41] framework to train the proposed SAR-DRN in the Windows 7 environment, 16 GB-RAM, with an Nvidia Titan-X (Pascal) GPU. The total training time costs about 4 h 30 min, which is less than SAR-CNN [28] with about 9 h 45 min under the same computational environment. …”

“…In this paper, rather than using log-transform [28] or modifying training loss function like [29], we propose a novel network for SAR image despeckling with a dilated residual network (SAR-DRN), which is trained in an end-to-end fashion using a combination of dilated convolutions and skip connections with a residual learning structure. Instead of relying on a pre-determined image, a priori knowledge, or a noise description model, the main superiority of using the deep neural network strategy for SAR image despeckling is that the model can directly acquire and update the network parameters from the training data and the corresponding labels, which need not manually adjust critical parameters and can automatically learn the complex internal non-linear relations with trainable network parameters from the massive training simulative data.…”

“…Unlike the methods proposed in [28,29], which utilize batch normalization to normalize the output features, SAR-DRN does not add this preprocessing layer, considering that the skip connections can also maintain the outputs of the data distribution in the different dilated convolution layers. The quantitative comparison of the two structures for SAR image despeckling is provided in Section IV.…”

“…Recently, borrowing the thought of the DnCNN model, Chierchia et al [28] also employed a set of convolutional layers named SAR-CNN, along with batch normalization (BN) and ReLU activation function, and a component-wise division residual layer to estimate the speckled image. As an alternative way of dealing with the multiplicative noise of SAR images, SAR-CNN uses the homomorphic approach with coupled logarithm and exponent transforms in combination with a similarity measure for speckle noise distribution.…”

“…To verify the proposed method, we compared the SAR-DRN method with four mainstream despeckling methods: The probabilistic patch-based (PPB) filter [13] based on patch matching, SAR-BM3D [14] based on 3-D patch matching and wavelet, SAR-POTDF [16] based on sparse representation, and SAR-CNN [28] based on the deep neural network. In the simulated-image experiments, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were employed as the quantitative evaluation indexes.…”