Multi-dimensional feature fusion network with transformer for image super-resolution

In the context of deep learning applied to single-image super-resolution, the quality of the reconstructed images is largely contingent upon the intricacy of the convolutional neural networks employed. However, this complexity is limited by the static nature of the receptive fields within these networks. Transformers, distinguished by their self-attention mechanism, are capable of capturing global dependencies, a feature that is beyond the reach of conventional CNNs. However, integrating Transformers into CNN architectures poses a challenge. This paper introduces an innovative solution, a Multi-Dimensional Feature Fusion Image Super-Resolution Network that harnesses Transformer's global representation capability. By utilizing the self-attention mechanism, our network achieves effective cross-feature extraction and establishes global dependencies throughout the feature map. A dedicated Multi-Dimensional Feature Fusion Module is employed to enhance feature fusion, further improving the reconstruction quality. Empirical evidence from experiments on the Set5 benchmark dataset reveals that our network outperforms existing state-of-the-art methods by 0.22dB in 4× super-resolution tasks, highlighting the efficacy of our approach.