A Review of Image Fusion Techniques

Bhataria, Karishma C; Shah, Bhumika

doi:10.1109/iccmc.2018.8487686

Cited by 14 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In early development on image fusion, spatial domain or transform domain methods were used commonly [ 21 , 22 , 23 , 24 ]. The spatial domain method is to process pixels directly.…”

Section: Introductionmentioning

confidence: 99%

“…The spatial domain method is to process pixels directly. These methods include Averaging, Brovey Method, Principal Component Analysis (PCA), Intensity-Hue-Saturation (IHS) [ 22 , 23 ]. The transform domain method requires Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

“…The transform domain method requires Fourier transform. The methods include Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Contourlet Transform (CT), Discrete Ripplet Transform (DRT) [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Bhataria [ 22 ] has reviewed both the fusion method of spatial domain and the transform domain. The PCA in spatial domain fusion provides a high information quality.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…In early development on image fusion, spatial domain or transform domain methods were used commonly [ 21 , 22 , 23 , 24 ]. The spatial domain method is to process pixels directly.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Bhataria [ 22 ] has reviewed both the fusion method of spatial domain and the transform domain. The PCA in spatial domain fusion provides a high information quality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, one model cannot capture enough information due to limitations of the system because it is hard to capture focus for all objects due to the limited focal length of camera. Therefore, combining images of different focal lengths for the same scenery by varying the focal length is known as multi-focus image fusion [8][9][10][11]. Similarly, CT and MRI images are used to diagnose many medical conditions This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Bhutto

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Image enhancement is a challenging task in image analysis particularly, it is more challenging in performing image fusion. Image fusion is the process of combining multiple images to produce quality output without any variation in contrast, blurring, and noise. Many image fusion algorithms have been implemented, but their final fused images suffer from variations in background contrast, uneven illumination, blurring, and the presence of noise. To overcome the aforementioned issues, this paper proposed a new image fusion method, which improves image contrast and also gives appropriate details of the image. Our method is based on a set of conventional techniques such as amalgamated histogram equalization and fast gray-scale grouping to handle the problems mentioned, and we improve overall fusion strategies by proposing a novel principal component analysis technique to convert RGB types images to high gray-scale contrast image as the final output image. We have carried out many experiments on different common databases used by various researchers. Our proposed method gives good subjective and objective performances compared to other statuses. Our proposed method can be used in different monitoring applications. INDEX TERMS Histogram equalization and fast gray-level grouping (HEFGLG), Image Fusion, Multifocus image, Infrared image, Visible image, Average pixel intensity, Cross Correlation.

show abstract

Medical image fusion based on local Laplacian decomposition and iterative joint filter

Chao

Wang

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Previous multi‐modal medical image fusion methods have suffered from color distortion, blurring, and noise. To address these problems, we propose a method for integrating the information contained in functional and anatomical medical images. In the proposed method, multi‐scale image representation of input images is produced by local Laplacian filtering. The rgb2ycbcr algorithm and iterative joint filters are then used to produce fused approximate images. The residual images are divided into regions of interest and noninterest regions, and then a local energy maximization scheme and local energy average scheme are used to combine these regions. Fused interest areas and fused noninterest areas are combined to produce fused residual images. Finally, an inverse local Laplacian filter is used as a reconstruction tool to produce a fused image. Experimental results indicated that our method has a distinct advantage over existing state‐of‐the‐art algorithms in terms of vision quality and objective metrics.

show abstract

A Review of Image Fusion Techniques

Cited by 14 publications

References 23 publications

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

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

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Medical image fusion based on local Laplacian decomposition and iterative joint filter

Contact Info

Product

Resources

About