The new polarization computational imaging method in deep learning mode leads to higher computational complexity and memory usage as the network depth increases and results in insufficient hierarchical feature extraction. To this end， a lightweight polarization computational imaging super-resolution network with cascade attention is proposed that requires fewer parameters and a lower computational complexity while ensuring the reconstruction accuracy. First， cascade and fusion connections are used to deepen the representational capabilities of the convolution layers to effectively transfer shallow features and reduce the number of parameters. Second， a spatial attention adaptive weighting mechanism is designed to extract polarized multi-parameter spatial content features. A spatial pyramid network is then constructed to enhance the polarization feature information under multiple receptive fields. An upsampling module introduces the shallow and deep reconstruction paths and generates high-resolution polarization images by fusing the features of the two-layer paths. Finally， the network end information refines the blocks to learn finer features and enhance the reconstruction quality. Experiments show that the texture details of the reconstructed images using the proposed method are more abundant. The peak signal-to-noise ratio （PSNR） of two-times super-resolution on the full polarized image set is 45.12 dB， and the number of parameters is approximately 9% of that for a multi-scale residual network （MSRN）. The proposed method effectively captures low-frequency feature information in a cascading manner while significantly reducing the number of parameters. Combined with the attention pyramid structure to explore deep features， an efficient super-resolution reconstruction is realized using a lightweight network.