![]() By incorporating the proposed component-wise division residual layer into the network, the convolutional layers are forced to learn the speckle component during the training process. To address this issue, a division residual method is leveraged in our method where a noisy SAR image is viewed as a product of speckle with the underlying clean image (i.e. As a result, the overall algorithm can not be learned in an end-to-end fashion. However, this approach needs extra steps to transfer the image into a logarithm space and from a logarithm space back to an image space. One possible solution to the despeckling problem would be to transform the image into a logarithm space and then learn the corresponding mapping via CNN. Using a specific CNN architecture, we learn a mapping from an input SAR image into a despeckled image. The despeckling CNN is adopted from our previous work on SAR image restoration. The detailed architecture of the despeckling sub-network is shown in Figure 3, where Conv, BN and ReLu stand for Convolution and Batch Normalization and Rectified Linear Unit, respectively. The composition of the two sub-networks, despeckling and colorization, forms the generator G in a typical generative adversarial network (GAN) framework as follows: The adversarial loss, empirically, can in principle become aware that gray looking outputs are unrealistic, and encourage a wider color distribution. ![]() Inspired by recent works on using generative adversarial learning for image colorization, we add the adversarial loss by introducing a discriminator network D. The colorization sub-network then transforms the despeckled image into a visible image. The primary goal of the despeckling sub-network is to restore a clean image from a noisy observation. The proposed method consists of three main components: despeckling sub-network G D, colorization sub-network G C and generative adversarial learning. In this section, we provide details of the proposed SAR-GAN method in which we aim to learn a mapping from input speckled SAR images to visible images for both noise removal and colorization. But in the case of SAR images, the input will have speckle and the expected output is the clean visible-like image with three RGB channels. Secondly, in the case of colorization techniques, in general noiseless grayscale images are given as input to First, in the image colorization domain (grayscale image to RGB) the luminance is directly given by grayscale input, so only the chrominance needs to be estimated. However, there are some notable differences. This problem shares some similarities with image colorization. Hence, generating a visible-like image from a SAR image is not only an interesting problem but also important for semantic segmentation and interpretation of SAR images. For example, even after despecking, it is difficult to distinguish between sandy land and grass field due to the grayscale nature of SAR images. Although state-of-the-art SAR image despecking algorithms such as SAR-BM3D and wavelet-based methods are able to generate despeckled SAR images with sharp edges, the resulting despeckled images are often difficult to interpret due to their grayscale nature.
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