Medical Video Super-Resolution Based on Asymmetric Back-Projection Network with Multilevel Error Feedback

Sheng Ren, Jianqi Li, Kehua Guo, Fangfang Li
2021 IEEE Access  
Medical video is important for medical diagnosis. However, due to the influence of the network bandwidth and hardware equipment, some medical videos have low resolution, which is not conducive for diagnosing early diseases with small lesions. Medical videos with super-resolutions can help doctors clearly observe small lesions and increase the likelihood of diagnosing and curing diseases. In this paper, we propose an advanced medical video super-resolution method based on an asymmetric
more » ... ction network. We construct a single-frame medical video super-resolution model as the benchmark model, combine the optical flow algorithm and multiframe fusion strategy to propose a medical video super-resolution method based on an asymmetric back-projection network with multilevel error feedback, and train high-quality and high-speed medical video super-resolution models. Finally, we use the proposed method to create superresolution versions of different types of low-resolution medical videos and evaluate the quality and efficiency of reconstruction. The experimental results show that the proposed medical video super-resolution method achieves superior performance compared to that of other methods considered in this work. INDEX TERMS Asymmetric back-projection network, medical diagnosis, multilevel error feedback, superresolution.
doi:10.1109/access.2021.3054433 fatcat:fyju5efvdbe5fd4q35i7m3d7vq