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Long Huang

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Proceedings Article | 14 February 2020 Paper

SRNet: a cascade network to speckle reduction of SAR image

Konghuai Shen, Xinglong Wang, Long Huang, Zhe Hu, Weidong Yang

Proceedings Volume 11428, 114280O (2020) https://doi.org/10.1117/12.2539382

KEYWORDS: Speckle, Synthetic aperture radar, Network architectures, Denoising, Image quality, Device simulation, Image segmentation, Image filtering, Visualization, Signal attenuation

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Speckle noise limits the usage of synthetic aperture radar (SAR) for object recognition and segmentation tasks. Most traditional methods sacrifice useful image information to achieve speckle reduction. The classic method based on local sliding window filtering has obvious side effect of erasing object edges and blurring texture information comparing with ground truth image. Another widely used method is convolutional neural network based on mean squared error, the visual effect of denoised image is not satisfactory even though MSE loss can have higher peak signal-to-noise ratio (PSNR) performance. In this paper, we present a cascade network to address this problem, namely SRNet, which employs an asymmetric architecture for the task of speckle noise reduction. The cascade architecture can supervise the network to revise on both pixel-wise level and feature-wise level by calculating correlation coefficient loss on the feature maps. In the meanwhile, we utilize the auxiliary loss on the intermediate results to accelerate the convergence of the network. The proposed network preserves the edge texture details much better than other compared methods.

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