scholarly journals Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 15750-15761 ◽  
Author(s):  
Chaoben Du ◽  
Shesheng Gao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Multi Scale ◽  
Focus Image

Multi-scale convolutional neural network for multi-focus image fusion

2019 ◽  
Vol 85 ◽  
pp. 26-35 ◽  
Author(s):  
Hafiz Tayyab Mustafa ◽  
Jie Yang ◽  
Masoumeh Zareapoor
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Multi Scale ◽  
Focus Image

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

scholarly journals Multi-Scale and Multi-Branch Convolutional Neural Network for Retinal Image Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 365
Author(s):  
Yun Jiang ◽  
Wenhuan Liu ◽  
Chao Wu ◽  
Huixiao Yao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Retinal Image ◽  
Spatial Information ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Feature Information ◽  
The Impact ◽  
Retinal Image Segmentation

The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information extraction. They are susceptible to the impact of the lesion area and poor image quality, resulting in the poor recovery of contextual information. This also causes the segmentation results of the model to be noisy and low in accuracy. Therefore, this paper proposes a multi-scale and multi-branch convolutional neural network model (multi-scale and multi-branch network (MSMB-Net)) for retinal image segmentation. The model uses atrous convolution with different expansion rates and skip connections to reduce the loss of feature information. Receiving domains of different sizes captures global context information. The model fully integrates shallow and deep semantic information and retains rich spatial information. The network embeds an improved attention mechanism to obtain more detailed information, which can improve the accuracy of segmentation. Finally, the method of this paper was validated on the fundus vascular datasets, DRIVE, STARE and CHASE datasets, with accuracies/F1 of 0.9708/0.8320, 0.9753/0.8469 and 0.9767/0.8190, respectively. The effectiveness of the method in this paper was further validated on the optic disc visual cup DRISHTI-GS1 dataset with an accuracy/F1 of 0.9985/0.9770. Experimental results show that, compared with existing retinal image segmentation methods, our proposed method has good segmentation performance in all four benchmark tests.

Multi-focus image fusion using pixel level deep learning convolutional neural network

2019 ◽  
Author(s):  
Manmay Rout ◽  
Siddheswar Nahak ◽  
Subhashree Priyadarshinee ◽  
Prasanti Santoshroy ◽  
Kodanda Dhar Sa ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Focus Image
Sign in / Sign up

Export Citation Format

Share Document