SAR Speckle Removal Using Hybrid Frequency Modulations

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Abstract
Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great impact on subsequent processing and analysis operations. To remove speckle artifacts, this article introduces a hybrid denoising approach by using a convolutional neural network (CNN) and consistent cycle spinning (CCS) in the nonsubsample shearlet transform (NSST) domain. First, we apply NSST to a noisy SAR image to gain low- and high-frequency coefficients. Second, we adopt a learned deep CNN model to eliminate the speckle noise in the low-frequency coefficients, which retains more contour information. Third, we employ CCS to enhance the high-frequency coefficients, which preserves more details of the original SAR image. Finally, we obtain the denoised image by using inverse NSST applied to the denoised coefficients. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only achieves better speckle removal performance but also maintains more detailed information retention.
Funding Information
  • Natural Science Foundation of China (61701310, 61401308, 61572063)
  • Natural Science Foundation of Hebei Province (F2020201025, F2016201187, F2018210148)
  • Science Research Project of Hebei Province (QN2020030, QN2016085)
  • Natural Science Foundation of Hebei University (2014-303)
  • Opening Foundation of Machine Vision Engineering of Hebei Province (2018HBMV02)

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