Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceDeep learning (DL) reconstruction networks are predominantly architectures that unroll traditional iterative algorithms and tend to perform better than non-unrolled models. Both types of models use convolutional neural networks (CNNs) as building blocks, but CNNs have the disadvantage of focusing on local relationships in the image. To overcome this, hybrid models have been proposed that combine CNNs with Transformers that focus on long-range dependencies. However, these hybrid transformers have been limited to non-unrolled reconstruction networks. Here, we propose an unrolled reconstruction network using a hybrid Transformer, Deep Cascade of Swin Transformer (DC-Swin), and verify that DC-Swin has high performance.
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