A Multi-focus Image Fusion Method Based on Image Information Features and the Artificial Neural Networks

2006 ◽  
pp. 747-752
Author(s):  
Lijian Zhou ◽  
Guangrong Ji ◽  
Changjiang Shi ◽  
Chen Feng ◽  
Rui Nian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Image Fusion ◽  
Fusion Method ◽  
Image Information ◽  
Focus Image ◽  
Artificial Neural ◽  
Image Fusion Method

scholarly journals Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2379
Author(s):  
Yin Dai ◽  
Yumeng Song ◽  
Weibin Liu ◽  
Wenhe Bai ◽  
Yifan Gao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Image Fusion ◽  
Image Classification ◽  
Test Accuracy ◽  
Fusion Method ◽  
Focus Image ◽  
Accuracy Rates ◽  
Image Fusion Method

Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification.

Gradient-based multi-focus image fusion method using convolution neural network

2021 ◽  
Vol 92 ◽  
pp. 107174
Author(s):  
Yang Zhou ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Kai Liu ◽  
Marco Anisetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Convolution Neural Network ◽  
Fusion Method ◽  
Gradient Based ◽  
Focus Image ◽  
Image Fusion Method

Multi-focus image fusion method based on two stage of convolutional neural network

2020 ◽  
Vol 176 ◽  
pp. 107681
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen ◽  
Pengxiang Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Fusion Method ◽  
Two Stage ◽  
Focus Image ◽  
Image Fusion Method

A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain

Optik ◽  
2015 ◽  
Vol 126 (20) ◽  
pp. 2508-2511 ◽  
Author(s):  
Jingjing Wang ◽  
Qian Li ◽  
Zhenhong Jia ◽  
Nikola Kasabov ◽  
Jie Yang
Keyword(s):  
Image Fusion ◽  
Contourlet Transform ◽  
Transform Domain ◽  
Fusion Method ◽  
Nonsubsampled Contourlet Transform ◽  
Focus Image ◽  
Image Fusion Method
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