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PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR image despeckling

Computers, Materials & Continua 2023年第9期76卷 3671-3689页

作者： Saima Yasmeen Muhammad Usman Yaseen Syed Sohaib Ali Moustafa M.Nasralla Sohaib Bin Altaf Khattak Department of Computer Sciences Comsats UniversityIslamabadPakistan Department of Research and Development Shearwater GeoservicesCrawleyWest SussexUK Department of Communications&Networks Engineering Prince Sultan UniversityRiyadhSaudi Arabia

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and *** convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle ***,these CNN-basedmethods have a *** do not decouple complex background information in ***,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile ***,extracting key speckle information in the presence of complex background is also a major problem with *** proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)*** primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex *** PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image *** utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling ***,the attention mechanism selectively focuses on key speckle features and facilitates complex background *** network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high *** comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration *** an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR *** source code for the PAN-DeSpeck network is available on GitHub.

关键词： Synthetic Aperture Radar(SAR) SAR image despeckling speckle noise deep learning pyramid networks multiscale image despeckling

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