Model training method, video fuzzy frame conversion method, equipment and storage medium

Abstract

The invention discloses a model training method, a video fuzzy frame conversion method, equipment and a storage medium, and relates to the field of artificial intelligence. The method comprises the steps of obtaining a sample video; extracting a training data pair from the sample video, wherein the training data pair comprises a sample clear frame and a sample fuzzy frame, and the sample clear frame and the sample fuzzy frame belong to the same video frame interval; inputting the training data pair into a generator of an image generation model to obtain a sample generation frame and sample optical flow information output by the generator; inputting the sample generation frame into a discriminator of the image generation model to obtain a discrimination result output by the discriminator; and training an image generation model according to the training data pair, the sample generation frame, the sample optical flow information and the discrimination result. When the image generation model obtained by training in the embodiment of the invention is used for converting the blurred video frames in the video, the time sequence consistency between the video frames can be kept, the conversion effect of converting the blurred video frames into the clear video frames is improved, and the quality of the converted video is further improved.

Description

technical field [0001] The embodiments of the present application relate to the field of artificial intelligence, and in particular to a model training method, a video blurred frame conversion method, a device, and a storage medium. Background technique [0002] Medical imaging is a technique for imaging the human body or specified parts of the human body in a non-invasive manner. Common medical imaging methods include ultrasound (Ultrasound) imaging, computerized tomography (CT) imaging, nuclear magnetic resonance (Nuclear Magnetic Resonance, NMR) imaging, etc. [0003] Affected by many factors, there may be a large number of artifacts in medical images obtained by medical imaging methods, resulting in poor quality of medical imaging; moreover, the contrast of medical images is low, and the edges of lesions are blurred, which is difficult for subsequent lesion segmentation and diagnosis. cause adverse effects. In related technologies, a pre-trained image generation model ...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a model training method, video blur frame conversion method, equipment, and storage medium, which can solve the problem of using the image generation model in the related art to convert blur video frames into clear video frames. The problem of ensuring the quality of converted medical videos

Images