Medical image super-resolution reconstruction method based on multi-attention residual feature fusion

Abstract

A medical image super-resolution reconstruction method based on multi-attention residual feature fusion combines two modules of a channel attention mechanism and a space attention mechanism, and enables a network to perform weighting processing on features of an input image, thereby solving the problem of insufficient network characterization capability caused by equal processing of the input features. A residual attention feature fusion module (RAFF) is provided, local features in the neural network are fused, and global features are fused, so that local residual features in the whole network can be more fully utilized, and the problem that the deep neural network is too deep and the learning ability of the network is insufficient is solved. According to the method, super-resolution reconstruction is effectively carried out on the medical image, and the image super-resolution reconstruction performance of a deep super-resolution network can be improved.

Description

technical field [0001] The invention relates to a medical image super-resolution reconstruction method based on multi-attention residual feature fusion. Background technique [0002] In recent years, deep convolutional neural networks have achieved very good results in super-resolution on a single image. A common method for image super-resolution is the instance-based method, which uses high-resolution images and low-resolution The high-resolution image information is used to generate a super-resolution version that approximates the original high-resolution image. There are also many methods of using super-resolution on medical images. The super-resolution reconstruction of medical images helps to improve the accuracy and objectivity of computer-aided clinical medical diagnosis and quantitative analysis of diseases. Therefore, the medical images that need It is necessary to perform super-resolution reconstruction. [0003] The defects of the existing technology are: most o...

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Problems solved by technology

[0004] In order to overcome the existing deficiencies, the present invention improves the above two problems, and proposes a medical image super-resolution reconstruction method based on multi-attention residual feature fusion, which combines channel attention mechanism and spatial attention The two modules of the mechanism allow the network to weight the features of the input image, so as to solve the problem of insufficient network representation ability caused by the equal processing of the input features; the residual attention feature fusion module (RAFF) is proposed, which combines the neural network Local feature fusion, coupled with global feature fusion, can make full use of the local residual features in the entire network, thereby solving the problem that the deep neural network is too deep and the network learning ability is insufficient

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