Image fusion at pixel and feature levels based on pyramid imaging

Ashalatha, B.; Reddy, M. Babu

doi:10.1109/icstm.2017.8089164

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…The process is used to reduce the volume of data, as well as to provide images that are more ideal and understandable by computers and humans. Image fusion also facilitates the collection of data from images derived from multiple sources to create high-quality fused images including all the spectral and spatial information [6].…”

Section: A Image Fusion Reviewmentioning

confidence: 99%

“…The input images are obtained from matching imaging devices, including various types of imaging devices, or from various other parameters such as infrared cameras and satellites. The resultant composite image is more useful in terms of the included information as compared to the individual images [6]. The techniques used in image fusion offer many benefits in different image processing tasks that rely on viewing more than one image of the same scene, such as object recognition and detection, as well as areas like digital photography and remote sensing, among others.…”

Section: Introductionmentioning

confidence: 99%

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Combining Multiple Seismic Attributes using Convolutional Neural Networks

Alotaibi¹,

Fadel²,

Jamal³

et al. 2022

IJACSA

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Seismic exploration involves estimating the properties of the Earth's subsurface from reflected seismic waves then visualizing the resulting seismic data and its attributes. These data and derived seismic attributes provide complementary information and reduce the amount of time and effort for the geoscientist. Multiple conventional methods to combine various seismic attributes exist, but the number of attributes is always limited, and the quality of the resulting image varies. This paper proposes a method that can be used to overcome these limitations. In this paper, we propose using Deep Learning-based image fusion models to combine seismic attributes. By using convolutional neural network (CNN) capabilities in feature extraction, the resulting image quality is better than that obtained with conventional methods. This work implemented two models and conducted a number of experiments using them. Several techniques have been used to evaluate the results, such as visual inspection, and using image fusion metrics. The experiments show that the Image-fusion Framework, using the Image Fusion Framework Based on CNN (IFCNN) approach, outperformed all other models in both quantitative and visual analysis. Its QAB/F and MS-SSIM scores are 50% and 10%, respectively, higher than all other models. Also, IFCNN was evaluated against the current state-of-the-art solution, Octree, in a comparative study. IFCNN overcomes the limitation of the Octree method and succeeds in combining nine seismic attributes with a better-combining quality, with QAB/F and NAB/F scores being 40% higher.

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Section: A Image Fusion Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Alotaibi¹,

Fadel²,

Jamal³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…The aim of image fusion is not only to minimize the amount of information, but also to create images that are more suitable and more comprehensible to human and computer perception. It gives the possibility to collect information from multisource images to generate a high-quality fused image with all spatial and spectral information [7].…”

Section: A Overview Of Image Fusionmentioning

confidence: 99%

“…Image fusion is used to combine two or more input images containing complementary details of the same scene creating a new image [6]. The input images are taken from the same imaging device with different parameters or from different imaging devices; the resulting fused image will be more informative than any individual input image [7]. Image fusion techniques show incredible benefits in various tasks of image processing that depend on viewing multiple images of the same scene such as object detection and recognition as well as in a variety of fields, for example digital photography, medical imaging and remote sensing [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…The input images are taken from the same imaging device with different parameters or from different imaging devices; the resulting fused image will be more informative than any individual input image [7]. Image fusion techniques show incredible benefits in various tasks of image processing that depend on viewing multiple images of the same scene such as object detection and recognition as well as in a variety of fields, for example digital photography, medical imaging and remote sensing [6], [7]. On these types of tasks, combining the significant details of multiple input images into a single fused image can often reduce the difficulty and enhance the outcome of the task [6].…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of 3D Seismic Images using Image Fusion Techniques

Alotaibi¹,

Fadel²,

Jamal³

et al. 2021

IJACSA

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Seismic images are data collected by sending seismic waves to the earth subsurface, recording the reflection and providing subsurface structural information. Seismic attributes are quantities derived from seismic data and provide complementary information. Enhancing seismic images by fusing them with seismic attributes will improve the subsurface visualization and reduce the processing time. In seismic data interpretation, fusion techniques have been used to enhance the resolution and reduce the noise of a single seismic attribute. In this paper, we investigate the enhancement of 3D seismic images using image fusion techniques and neural networks to combine seismic attributes. The paper evaluates the feasibility of using image fusion models pretrained on specific image fusion tasks. These models achieved the best results on their respective tasks and are tested for seismic image fusion. The experiments showed that image fusion techniques are capable of combining up to three seismic attributes without distortion, future studies can increase the number. This is the first study conducted using pretrained models on other types of images for seismic image fusion and the results are promising.

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Image Fusion Survey: A Comprehensive and Detailed Analysis of Image Fusion Techniques

Manviya

Bharti

2020

Social Networking and Computational Intelligence

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Image fusion at pixel and feature levels based on pyramid imaging

Cited by 3 publications

References 2 publications

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Enhancement of 3D Seismic Images using Image Fusion Techniques

Image Fusion Survey: A Comprehensive and Detailed Analysis of Image Fusion Techniques

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