Optimization of endometrial tolerance ultrasound image reconstruction algorithm supported by machine vision technology

In today's society, hospitals are treated with images generated by medical examination equipment for disease diagnosis, and high-resolution images can greatly improve the accuracy of doctors' disease diagnosis.The study constructs an ultrasound image dataset US-Dataset suitable for the task of superresolution reconstruction of ultrasound images.Based on this ultrasound image dataset, a degradation model is proposed, which in turn constructs ultrasound image matching pairs containing high -low resolution images for training the model proposed in this paper.To improve the perceptual quality of endometrial images, a super-resolution reconstruction model UN-SRGAN based on generative adversarial network is proposed in this paper.The network structure of this model consists of a generator and a discriminator.To validate the effectiveness of the model proposed in this paper, it is evaluated on Accuracy, Precision, Recall, Specificity, and F1-score metrics.The proposed model achieves the lead on PSNR and SSIM metrics and subjective quality evaluation, and the UN-SRGAN model has an accuracy of 0.9721, which is better than the other models, verifying the effectiveness of the model.