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Fully polarimetric synthetic aperture radar imagery (PolSAR) can provide rich polarimetric information, however, due to the limitations of the system"s signal bandwidth and the physical size of the antenna, the spatial resolution of the SAR imaging system is restricted while acquiring multiple polarization information. To solve this problem, based on the deep learning framework, this paper proposes a multi-scale attention-based PolSAR super-resolution network (MS-PSRN), which performs super-resolution reconstruction on the low-resolution full-polarimetric SAR images to generate the fully polarimetric SAR images with high spatial resolution. Under this super-resolution reconstruction framework, this paper uses a multi-scale architecture to fully extract the feature information of objects at different scales. On this basis, the spatial attention mechanism and the channel attention mechanism are introduced to recalibrate the feature maps, which is used to enhance the reconstruction performance of spatial details and improve the ability to maintain polarization information respectively. Two attention mechanism embedding methods, joint and separated, are proposed to cope with the spatial size and quantity changes of the feature maps processed by the encoder and decoder. This paper introduces a residual information distillation mechanism, extracts discriminative features through feature distillation, and compresses model parameters at the same time. In addition, the adaptive loss function is proposed to constrain the network training process and improve the model"s numerical fitting ability and edge information preservation ability. In this paper, the proposed method is verified by two data sets, which is the simulated data and the real data produced by RADARSAT-2 images. The experimental results of spatial information show that the proposed method is superior to the comparison algorithms in both visual results and quantitative indicators, and has higher texture detail reconstruction accuracy and lower reconstruction error. The polarimetric information preservation test shows that the proposed method can effectively preserve the polarimetric information of PolSAR images while improving the spatial resolution.