A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution

In recent years, deep learning super-resolution models for progressive reconstruction have achieved great success. However, these models which refer to multi-resolution analysis basically ignore the information contained in the lower subspaces and do not explore the correlation between features in the wavelet and spatial domain, resulting in not fully utilizing the auxiliary information brought by multi-resolution analysis with multiple domains. Therefore, we propose a super-resolution network based on the wavelet multi-resolution framework (WMRSR) to capture the auxiliary information contained in multiple subspaces and to be aware of the interdependencies between spatial domain and wavelet domain features. Initially, the wavelet multi-resolution input (WMRI) is generated by combining wavelet sub-bands obtained from each subspace through wavelet multi-resolution analysis and the corresponding spatial domain image content, which serves as input to the network. Then, the WMRSR captures the corresponding features from the WMRI in the wavelet domain and spatial domain, respectively, and fuses them adaptively, thus learning fully explored features in multi-resolution and multi-domain. Finally, the high-resolution images are gradually reconstructed in the wavelet multi-resolution framework by our convolution-based wavelet transform module which is suitable for deep neural networks. Extensive experiments conducted on two public datasets demonstrate that our method outperforms other state-of-the-art methods in terms of objective and visual qualities.