| Citation: | OU Hai-ning, LI Chang-di, ZENG Rui-bin, WU Yan-feng, LIU Jia-ning, CHENG Peng. DnCNN-RM: an adaptive SAR image denoising algorithm based on residual networks[J]. Chinese Optics. doi: 10.37188/CO.EN-2024-0028
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In the field of image processing, the analysis of Synthetic Aperture Radar (SAR) images is crucial due to its broad range of applications. However, SAR images are often affected by coherent speckle noise, which significantly degrades image quality. Traditional denoising methods, typically based on filter-based techniques, often face challenges related to inefficiency and limited adaptability. To address these limitations, this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture, with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments. The proposed algorithm integrates residual network modules, which directly process the noisy input images to generate denoised outputs. This approach not only reduces computational complexity but also mitigates the difficulties associated with model training. By combining the Transformer module with the residual block, the algorithm enhances the network's ability to extract global features, offering superior feature extraction capabilities compared to CNN-based residual modules. Additionally, the algorithm employs the adaptive activation function Meta-ACON, which dynamically adjusts the activation patterns of neurons, thereby improving the network's feature extraction efficiency. The effectiveness of the proposed denoising method was empirically validated using real SAR images from the RSOD dataset. The proposed algorithm exhibited remarkable performance in terms of EPI, SSIM, and ENL, while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms. The PSNR performance was enhanced by over twofold. Moreover, the evaluation of the MSTAR SAR dataset substantiated the algorithm's robustness and applicability in SAR denoising tasks, with a PSNR of
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