Abstract: Medical ultrasound imaging is one of the most widely used methods in clinical diagnosis.However,the resolution and contrast of ultrasound images are low,and the noise is serious.Image Super-resolution is widely used to improve the quality of ultrasound images.However,the exsisting studies lack full use of complementary information in ultrasound video frames,so the results are not ideal.To solve this problem,this paper proposes a super-resolution method of medical ultrasound images based on video multi-frame fusion.Firstly,it builds a multi-frame fusion model based on convolutional neural network for ultrasound images.The model obtains the fused image with rich information by feature fusion of continuous multi-frame images.Then,it builds a lightweight image super-resolution model based on data-free knowledge distillation.The model trains fused feature images to obtain a teacher super-resolution model.To obtain the final high-quality medical ultrasound image,it creates a lightweight student super-resolution model using the trained teacher model and training data from the generative adversarial network.Finally,the proposed method is tested on large ultrasound datasets,using two objective image evaluation indicators and results on image classification to evaluate its performance.The results show that compared with other methods,the proposed method not only improves the resolution of ultrasound images,but also obtains ultrasound images with more information and higher contrast.The recognition accuracy of the super-resolution image obtained by this method in the classification network can reach 97.30%,which is much higher than that of previous approaches and can increase productivity and the precision of clinical diagnoses.

Key words: Medical ultrasound image, Multi-frame fusion, Super-resolution reconstruction, Data-free knowledge distillation, Convolutional neural network