基于生成对抗网络的SAR解压缩图像重建算法*

doi:10.7523/j.ucas.2023.033

摘要/Abstract

摘要： SAR图像的高倍数压缩处理会导致图像中目标和纹理信息受损,解压缩后的SAR图像会出现细节模糊、目标不易分辨等问题,难以有效反映真实的地物特征。为解决上述问题,基于生成对抗网络架构,提出了一种新的SAR图像重建算法,该算法基于编解码结构,将卷积神经网络与自注意力机制并行融合作为生成器,设计了简洁高效的ConTransformer,从而得到更丰富的全局特征,有效提升小目标重建效果。针对判别网络,在U-Net特征提取器中引入谱归一化,降低模型对输入扰动的敏感程度,可以达到抑制伪影的效果;同时引入预训练掩码机制,加强高层次语义特征提取,提升重建图像真实性。实验证明该方法所得到的重建图像比Real-ESRGAN等基于生成对抗网络的经典方法所得重建结果具有更为清晰的视觉效果,且关键性能指标值更为出色,其中峰值信噪比提升了0.57dB~1.54dB。

关键词: SAR解压缩图像, 生成对抗网络, ConTransformer编码器, 掩码机制

Abstract: The high multiple compression processing of SAR image will cause damage to the target and texture information in the image, which makes the problems of blur and indistinguishable targets often appear in the decompressed SAR image, and is difficult to effectively reflect the real features of ground objects. To solve the above problems, a new SAR image reconstruction algorithm is proposed based on the generative adversarial network. Based on the codec structure, the algorithm takes the parallel fusion of convolutional neural network and self-attention mechanism as the generator, and designed a simple and efficient architecture called ConTransformer, which can get richer global features and effectively improve the effect of small-target reconstruction. For the Discriminator, spectral normalization is introduced into the U-Net feature extractor to reduce the sensitivity of the model to input disturbance, so as to suppress artifacts. Synchronously, the pre-training mask mechanism is used to enhance the extraction of high-level semantic features and improve the authenticity of reconstructed images. Experimental results show that the reconstructed images obtained by this method have clearer visual effects and better key performance indexes than those obtained by Real-ESRGAN and other typical methods based on generative adversarial networks, among which the peak signal-to-noise ratio is improved by 0.57dB~1.54dB.

Key words: SAR decompressed image, generative adversarial networks(GAN), contransformer encoder, masking mechanism

中图分类号:

TP751.1

张冰玉, 潘志刚, 姚锴, 董旭彬. 基于生成对抗网络的SAR解压缩图像重建算法^*[J]. 中国科学院大学学报, DOI: 10.7523/j.ucas.2023.033.

ZHANG Bingyu, PAN Zhigang, YAO Kai, DONG Xubin. SAR decompressed image reconstruction algorithm based on generative adversarial network[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2023.033.

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基于生成对抗网络的SAR解压缩图像重建算法^*

SAR decompressed image reconstruction algorithm based on generative adversarial network

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