DnCNN-RM：基于残差网络的自适应合成孔径雷达图像去噪算法

欧海宁; 李长頔; 曾瑞彬; 吴言枫; 刘佳宁; 程鹏

doi:10.37188/CO.EN-2024-0028

DnCNN-RM：基于残差网络的自适应合成孔径雷达图像去噪算法

1.
湄洲湾职业技术学院自动化工程系, 福建莆田, 351100
2.
苏州占空比信息科技有限公司, 江苏苏州, 215000
3.
中国科学技术大学, 计算机科学与技术学院, 安徽合肥, 230000

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出版历程
- 收稿日期: 2024-10-10
- 录用日期: 2025-01-17
- 网络出版日期: 2025-01-26

DnCNN-RM: an adaptive SAR image denoising algorithm based on residual networks

doi: 10.37188/CO.EN-2024-0028

1.
Department of Automation Engineering, Meizhouwan Vocational Technology College, Putian, 351100
2.
Suzhou Duty Ratio Information Technology Co., Ltd., Suzhou, 215000
3.
School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230000

More Information

Author Bio:
欧海宁（1979—），男，福建省莆田人，本科学士，2002年于华侨大学获得学士学位。现就职于湄洲湾职业技术学院，主要从事自动化与机器视觉研究。E-mail：mzyxqb@126.com

曾瑞彬（1994—），男，福建宁德人，硕士研究生，2023年于中国科学院大学获得硕士学位。现就职于湄洲湾职业技术学院，主要从事人工智能研究。E-mail：zengruibin19@mails.ucas.ac.cn

Corresponding author: zengruibin19@mails.ucas.ac.cn

摘要

摘要:
在图像处理领域，合成孔径雷达（SAR）图像的分析由于其广泛的应用而具有重要意义。然而，这些图像常常受到相干斑噪声的干扰，显著降低了图像质量。传统的去噪方法，通常基于滤波器技术，往往存在效率低下和适应性差的局限性。为了克服这些不足，本文提出了一种基于增强残差网络架构的SAR图像去噪算法，旨在提升SAR图像在复杂电磁环境中的应用效果。该算法结合了残差网络模块，直接对含噪输入图像进行处理，生成去噪输出。这种方法不仅有效降低了计算复杂度，还缓解了模型训练过程中遇到的困难。通过将Transformer模块与残差块结合，该算法提高了网络对全局特征的提取能力，相较于基于卷积神经网络（CNN）的残差模块，具有更强的特征提取能力。此外，算法引入了自适应激活函数Meta-ACON，能够动态调整神经元的激活模式，从而进一步提升了网络在特征提取上的效率。通过在RSOD数据集上的实验证明，所提出的去噪方法在EPI、SSIM和ENL等指标上表现出色，同时在PSNR方面也取得了显著的提升。与传统的去噪算法及深度学习算法相比，该算法的PSNR性能提高了两倍以上。进一步在MSTAR SAR数据集上的测试，得到了PSNR值为25.2021，验证了该算法在SAR去噪领域的良好泛化性。这些结果表明，所提出的算法s不仅能够有效降低相干斑噪声，还能有效保留SAR图像中的关键信息特征，从而在实际应用中显著提高图像质量和可用性。
- 合成孔径雷达图像 /
- 图像去噪 /
- 残差网络 /
- 自适应激活函数
Abstract:
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 25.2021 being attained. These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery, thereby enhancing its quality and usability in practical scenarios.
- SAR images /
- image denoising /
- residual networks /
- adaptive activation function

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Figure 1. Residual unit module structure diagram

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Figure 2. Schematic diagram of Meta-ACON in non-linear and linear switching

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Figure 3. Structure of the neural network for learning the switching factor

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Figure 4. DnCNN-RM network structure diagram

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Figure 5. The denoising results of SAR1 by different denoising algorithms

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Figure 6. The denoising results of SAR2 by different denoising algorithms

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Table 1. Denoising results of SAR1 by different SAR image denoising algorithms

Method	Mean	Std	PNSR	EPI	SSIM	ENL
Lee	118.44	13.73	14.74	0.987	0.996	0.999
Frost	115.32	10.74	12.49	0.833	0.979	0.999
SAR-BM3D	292.84	17.11	51.74	0.992	0.992	0.999
DnCNN	607.76	25.38	22.56	1.000	0.988	0.999
DnCNN-RM	697.12	25.22	59.011	1.000	0.999	0.999

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Table 2. Denoising results of SAR2 by different SAR image denoising algorithms

Method	Mean	Std	PNSR	EPI	SSIM	ENL
Lee	556.73	23.38	18.38	0.989	0.995	0.999
Frost	409.82	20.24	24.55	0.735	0.978	0.999
SAR-BM3D	595.95	24.41	21.45	1.000	0.997	0.999
DnCNN	1180.49	34.36	23.40	1.000	0.987	0.999
DnCNN-RM	1150.26	35.65	39.78	0.999	0.999	0.999

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Table 3. Denoising results of various denoising algorithms in ablation study

Method	PSNR
DnCNN	15.5135
DnCNN+ ResNet Block (CNN)	24.5698
DnCNN + ResNet Block (Transformer)	24.8080
DnCNN + Meta-ACON	24.5833
DnCNN+ ResNet Block (CNN) + Meta-ACON	24.5888
DnCNN + ResNet Block (Transformer) + Meta-ACON	25.2021

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Table 4. Comparison results of SAR image denoising with different algorithms

Method	Wang et al.^[14]	Zhang et al.^[15]	Wang^[16]	Ours
PSNR	22.7	22.44	22.90	25.201
SSIM	0.709	0.900	0.909	0.951

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