The high multiple compression processing of SAR images 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 designs 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 indicators 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 0.57-1.54 dB.
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