Image Fusion Methods: A Survey

Masood, Saima; Sharif, Muhammad; Yasmin, Mussarat; Shahid, Muhammad Alyas; Rehman, Amjad

doi:10.25103/jestr.106.24

Cited by 30 publications

(7 citation statements)

References 83 publications

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“…Image fusion is a technique used to extract useful data from many input photographs and merge it to create a new output image that is more descriptive and useful than the sum of the input images [26]. Image fusion reduces data size, keeps vital features, and provides a more accessible image [27].…”

Section: Fusion Image By (Ihs) Transformationsmentioning

confidence: 99%

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Atiyah¹,

Hassan²

2023

JESA

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A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.

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Section: Fusion Image By (Ihs) Transformationsmentioning

confidence: 99%

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Atiyah¹,

Hassan²

2023

JESA

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“…The theories and transformations which are used to apply the image fusion is very diverse like wavelet transform [19][20][21][22][23][24][25] and IHS Transformation [26], Discrete cosine transform [27][28], curvelet transform based on linear dependency test [29]. Another side of approaches are depending of different focus depth of input image which called multi-focus for image fusion are described in [30][31][32]. Other researches are explained the filtration operation, modeling and segmentation for image [33][34][35][36][37] While ICA independent component analysis can also be used for executing a fusion in a series of images [38].…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Digital Image Fusion and its Application

Al-Mokhtar¹,

Ibraheem²,

Al-Layla³

2021

(AREJ)

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Image Fusion is applied to get back a group of data from two or more than two images and put it into one image to create additional wealthy information and profitable more than any of the input data that led to increase the features and performance of information. The quality of the resultant data relies on the implementation of the process. Image fusion is excessively utilized in stereo camera fusion, medical application, manufacture process monitoring, electronic circuit design and inspection, complex machine diagnostics and in intelligent robots on assembly lines. This study displays a literature review on different types of algorithm and theories which apply on images. Many quality criteria have debated to do a brief comparison of these methods. The applications of image fusion are showed in this paper.

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“…Many studies have been published in different areas to fused two different modalities. [ 1 2 ] In recent years, deep learning networks have many applications in different fields such as computer vision and image processing problems such as classification,[ 3 ] segmentation,[ 4 ] registration,[ 5 ] super-resolution. [ 6 ] There are a lot of methods that fused medical images as PET, SPECT, MRI, and CT.[ 7 8 9 10 ] Image fusion based on deep learning methods has also become a new common topic.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)

Amini

Mostaar

2022

J Med Signals Sens

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Background: The fusion of images is an interesting way to display the information of some different images in one image together. In this paper, we present a deep learning network approach for fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Methods: We fused two MRI and PET images automatically with a pretrained convolutional neural network (CNN, VGG19). First, the PET image was converted from red-green-blue space to hue-saturation-intensity space to save the hue and saturation information. We started with extracting features from images by using a pretrained CNN. Then, we used the weights extracted from two MRI and PET images to construct a fused image. Fused image was constructed with multiplied weights to images. For solving the problem of reduced contrast, we added the constant coefficient of the original image to the final result. Finally, quantitative criteria (entropy, mutual information, discrepancy, and overall performance [OP]) were applied to evaluate the results of fusion. We compared the results of our method with the most widely used methods in the spatial and transform domain. Results: The quantitative measurement values we used were entropy, mutual information, discrepancy, and OP that were 3.0319, 2.3993, 3.8187, and 0.9899, respectively. The final results showed that our method based on quantitative assessments was the best and easiest way to fused images, especially in the spatial domain. Conclusion: It concluded that our method used for MRI-PET image fusion was more accurate.

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Image Fusion Methods: A Survey

Cited by 30 publications

References 83 publications

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

A Review of Digital Image Fusion and its Application

Deep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)

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