A fuzzy convolutional neural network for enhancing multi-focus image fusion
The images captured by the cameras contain distortions, misclassified pixels, uncertainties
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …
A self-supervised residual feature learning model for multifocus image fusion
Multi-focus image fusion (MFIF) attempts to achieve an “all-focused” image from multiple
source images with the same scene but different focused objects. Given the lack of multi …
source images with the same scene but different focused objects. Given the lack of multi …
Multi-focus image fusion using neutrosophic based wavelet transform
The key aim of Multi-focus Image Fusion (MFIF) is to gather all the necessary and useful
information as well as features from the source images and then merge that information to …
information as well as features from the source images and then merge that information to …
Automatic determination of digital modulation types with different noises using convolutional neural network based on time–frequency information
In this study, a novel digital modulation classification model has been proposed for
automatically recognizing six different modulation types including amplitude shift keying …
automatically recognizing six different modulation types including amplitude shift keying …
A novel approach in multimodality medical image fusion using optimal shearlet and deep learning
V Subbiah Parvathy, S Pothiraj… - International Journal of …, 2020 - Wiley Online Library
Multi‐modality medical image fusion (MMIF) procedures have been generally utilized in
different clinical applications. MMIF can furnish an image with anatomical as well as …
different clinical applications. MMIF can furnish an image with anatomical as well as …
A novel approach for multi-focus image fusion based on SF-PAPCNN and ISML in NSST domain
L Li, Y Si, L Wang, Z Jia, H Ma - Multimedia Tools and Applications, 2020 - Springer
In order to further improve the contrast and sharpness of fused image, a novel multi-focus
image fusion algorithm based on spatial frequency-motivated parameter-adaptive pulse …
image fusion algorithm based on spatial frequency-motivated parameter-adaptive pulse …
A novel multi-focus image fusion by combining simplified very deep convolutional networks and patch-based sequential reconstruction strategy
C Wang, Z Zhao, Q Ren, Y Xu, Y Yu - Applied Soft Computing, 2020 - Elsevier
Multi-focus image fusion is an important approach to obtain the composite image with all
objects in focus, and it can be treated as an image segmentation problem, which is solved …
objects in focus, and it can be treated as an image segmentation problem, which is solved …
Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method
Estimating the impacts of traffic control strategies (TCSs) can provide feedback in traffic
control and help to identify the effective ones among massive strategies, thus boosting …
control and help to identify the effective ones among massive strategies, thus boosting …
Semisupervised remote sensing image fusion using multiscale conditional generative adversarial network with siamese structure
Remote sensing image fusion (RSIF) can generate an integrated image with high spatial
and spectral resolution. The fused remote sensing image is conducive to applications …
and spectral resolution. The fused remote sensing image is conducive to applications …
A medical image fusion method based on SIFT and deep convolutional neural network in the SIST domain
L Wang, C Chang, Z Liu, J Huang… - Journal of Healthcare …, 2021 - Wiley Online Library
The traditional medical image fusion methods, such as the famous multi‐scale
decomposition‐based methods, usually suffer from the bad sparse representations of the …
decomposition‐based methods, usually suffer from the bad sparse representations of the …