Object three-dimensional drawing method based on medical education

Abstract

The invention provides an object three-dimensional drawing method based on medical education. The method comprises the steps that high-resolution image blocks and low-resolution image blocks are collected through a medical image scanning device; calculating by a deep learning engine to obtain a plurality of candidate high-resolution pixel sets and reference high-resolution pixel sets; the deep learning engine generates a post-processing residual value; a pixel summation unit performs pixel summation operation on the post-processing residual value and the low-resolution image block to obtain forward and reverse prediction high-resolution image blocks; a bi-directional predictor generates a super-resolution tile for the forward predicted high-resolution tile and the reverse predicted high-resolution tile. According to the object three-dimensional drawing method based on medical education, the three-dimensional image quality and the operation efficiency are effectively improved with low software and hardware cost, and meanwhile the problems of image distortion of an existing reconstruction method and limited bandwidth and limited storage space of an existing remote reconstruction method are solved.

Description

technical field [0001] The invention relates to three-dimensional image reconstruction, in particular to an object three-dimensional rendering method based on medical education. Background technique [0002] High-resolution 3D images have higher quality and clear details, which are very important for applications such as medical imaging. The medical image scanning system needs to record the model file and compress the image and then transmit it to the cloud. After the cloud decompresses the model file, it can be transmitted to the medical terminal for real-time monitoring by the user. Multiple model files collected for users to call at any time. However, better image fluency and more detailed image information will inevitably require an increase in the resolution and frame rate of the model file, but the capacity of the model file will also be increased, resulting in a greater burden on cloud storage space and cache. If technologies such as super-resolution reconstruction ...

AI Technical Summary

Problems solved by technology

However, better image fluency and more detailed image information will inevitably require an increase in the resolution and frame rate of the model file, but the capacity of the model file will also increase as a result, resulting in a greater burden on cloud storage space and cache

If super-resolution reconstruction and other technologies are used, the image edge often has distortion problems such as jagged phenomenon

In addition, the reconstruction method of the existing scheme still has the problem of low signal-to-noise ratio, and it is impossible to propose a suitable image enhancement mechanism for the medical image scanning system. Therefore, it is necessary to provide a cloud-based storage space, cache size, network speed and image quality. A method for high-resolution 3D image reconstruction

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