SAR image despeckling using a CNN guided by high-frequency information

Abstract

Preserving image details is a challenging and significant task for synthetic aperture radar (SAR) image despeckling. In this paper, we proposed a high-frequency information extraction module based on block-matching and 3D filtering for SAR image despeckling (SAR-BM3D) and wavelet transformation to guide the denoisers based on a convolutional neural network (CNN). Firstly, SAR-BM3D is employed to conduct the initial speckle removal, and then Haar wavelet transformation takes the task of extracting high-frequency information of the initial despeckled image. The noisy image and the high-frequency information are merged together to be the input of the CNN, which makes it easier for the CNN-based denoisers to restore the textures and details of the SAR image. Extensive experiments on synthetic and real SAR images have been validated, which show that our method can effectively remove the speckle noise and improve the detail-preserving capacity of the CNN-based denoiser.

KEYWORDS:

• Convolutional neural network (CNN)
• wavelet transformation
• synthetic aperture radar (SAR)
• despeckling
• block-matching and 3D filtering for SAR image despeckling (SAR-BM3D)
• high-frequency information

Disclosure statement

No potential conflict of interest was reported by the author(s).