基于深度卷积神经网络的图像超分辨率重建

Super-resolution Reconstruction of Medical Images Based on Deep Convolutional Neural Networks

现如今,科学技术飞速发展,现代科技在医学领域发挥着重要作用。但由于硬件设备成本过高,目前所获取的医学图像并不是很清晰。所以,如何改善医学图像不清晰的问题是非常有价值的。本文以医疗图像作为研究对象,运用深度学习技术,在SRCNN网络的基础上,建立一种面向高分辨的医疗影像的深度学习模式,旨在提高分辨率。实验证明:采用该方法可以对医疗影像进行高分辨率重构,其效果比插值法方法更好。

Nowadays,China's science and technology are developing rapidly,and modern science and technology play an important role in the field of medicine.However,due to the high cost of hardware equipment,the medical images currently obtained are not very clear.Therefore,how to improve the problem of unclear medical images is very valuable.Taking medical images as the research object,this paper uses deep learning technology to establish a deep learning mode for high-resolution medical images based on SRCNN network,aiming to improve the resolution.Experiments show that this method can perform high-resolution reconstruction of medical images,and its effect is better than that of the interpolation method.