Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform

@article{Deeba2021MultimodalBoostMM,
  title={Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform},
  author={Farah Deeba and Fayaz Ali Dharejo and Muhammad Zawish and Yuanchun Zhou and Kapal Dev and Sunder Ali Khowaja and Nawab Muhammad Faseeh Qureshi},
  journal={IEEE/ACM transactions on computational biology and bioinformatics},
  year={2021},
  volume={PP}
}
Multimodal medical images are widely used by clinicians and physicians to analyze and retrieve complementary information from high-resolution images in a non-invasive manner. Loss of corresponding image resolution adversely affects the overall performance of medical image interpretation. Deep learning-based single image super resolution (SISR) algorithms have revolutionized the overall diagnosis framework by continually improving the architectural components and training strategies associated… 

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