Medical image super-resolution reconstruction method based on dense mixed attention network

Abstract

The invention discloses a medical image super-resolution reconstruction method based on a dense mixed attention network. According to the method, a hybrid attention mechanism is introduced on the basis of the dense neural network, so that the neural network pays more attention to channels and regions containing rich high-frequency information, network convergence is accelerated, and the super-resolution precision is further improved. The method mainly comprises the following steps: designing and constructing a network based on a dense neural network and a hybrid attention mechanism; preprocessing the data set, enhancing the data, and constructing a training sample; training the network model by using L2 loss until the network model is converged; and in a super-resolution reconstruction stage, inputting a low-resolution medical image, and performing super-resolution reconstruction by using the trained network model to obtain a final high-resolution image. Compared with a mainstream super-resolution method, the method provided by the invention is higher in precision, and is an effective medical image super-resolution reconstruction method.

Description

technical field [0001] The invention relates to a medical image super-resolution reconstruction technology, in particular to a medical image super-resolution reconstruction method based on a dense mixed attention network, belonging to the field of digital image processing. Background technique [0002] Medical images are widely used in clinical diagnosis and treatment, but in the process of obtaining medical images, due to hardware limitations and environmental influences, the lack of high-frequency information leads to low resolution and blurred medical images. Improving the above problems from the perspective of hardware is limited by the manufacturing process and cost. Improvement from the perspective of software, using image super-resolution methods to perform super-resolution reconstruction of low-resolution medical images can efficiently obtain corresponding high-resolution images. [0003] There are three main types of current image super-resolution methods, which are...

AI Technical Summary

Problems solved by technology

In summary, there is still room for improvement in the performance of learning-based image super-resolution methods

Images