Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

Abas, Asan Ihsan; Baykan, Nurdan Akhan

doi:10.18280/ts.380201

Cited by 8 publications

(8 citation statements)

References 49 publications

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“…To demonstrate the efficacy of our method, we compared it to other newly developed fusion methods [27], including as: The pulse coupled convolutional neural network (PCNN), the adaptive -parameter PA-PCNN in NSST domain (NSST-PAPCNN) [14], the image gradient motivation PCNN in NSCT domain (NSCT-G-PCNN) [15], and in the NSST domain, the bounded measured PCNN technique BM-PCNN-NSST [17]. The parameters settings in our experience as follows:…”

Section: Results and Analysismentioning

confidence: 99%

An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

Abdelfatih¹,

Ismail²

2022

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The neurodegenerative disease such as: Parkinson's disease (PD), mild Alzheimer’s affects many people and has a serious influence on their life, With the quick advancement of computer-aided diagnostic (CAD) methods, early detection is crucial since effective treatment halts the spread of the disease. Image fusion is useful for medical diagnostics. In this paper we propose a multi-modality medical image fusion algorithm in NSST domain. Shearlets (NSST) are decomposed similarly to contourlets (NSCT), except that instead of applying the Laplacian pyramid followed by directional filtering, shearlets use a shear matrix. In this article the Biorthogonal CDF9/7 filter is applied in the shift-invariant shearlet filter banks, then the coefficients of low frequency bands are selected using maximum rule, and using the gradient in each subband high frequency image to motivate the modified pulse coupled neural networks (Modified PCNN). Finally reverse IHS to get the fused color image, all this to optimize the calculation performance and improve the characteristics of the fused image for medical diagnosis. Our approach was validated with several brain diseases modalities: Alzheimer’s…etc. The findings reveal that the suggested image fusion technique has a higher quality than those fused by previous algorithms existing.

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Section: Results and Analysismentioning

confidence: 99%

An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

Abdelfatih¹,

Ismail²

2022

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“…Make

where N is the total number of layers of the LP and LP l is the l -th layer image decomposed by the LP . Pyramid is formed by LP 0 , LP 1 , ..., LP N ; any layer image of GP is the changed value of the base layer and its previous layer image after expansion [ 21 ]. The specific reconstruction process is as follows:

…”

Section: Methodsmentioning

confidence: 99%

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network

2022

Computational Intelligence and Neuroscience

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With the continuous development of imaging sensors, images contain more and more information, the images presented by different types of sensors are different, and the images obtained by the same type of sensors under different parameters or conditions are also different. Multisource image fusion technology combines images acquired by different types of sensors or the same type of sensors with different parameter settings, which makes the image information more complete, compensates for the limitations of images of the same type, and also allows you to save information about the characteristics of the original image. Multimodal image mosaic and multifocal image mosaic have been studied in detail in two directions. On the one hand, a method based on frequency domain transformation is used for multiscale image decomposition. On the other hand, image extraction with neural network-based methods is proposed. The technology of convolutional neural networks (CNNs) allows to extract richer texture features. However, when using this method for fusion, it is difficult to obtain an accurate decision map, and there are artifacts in the fusion boundary. Based on this, a multifocal fusion method based on a two-stage CNN is proposed. Train the advanced intensive network to classify input image blocks as focus, and then use the appropriate merge rules to get the ideal decision tree. In addition, several versions of the fuzzy learning set have been developed to improve network performance. Experimental results show that the frames of the first stage proposed by the algorithm make it possible to obtain an accurate decision scheme and that the frames of the second stage make it possible to eliminate the pseudo-shadow of the integration boundary.

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“…With the advancement of technology, deep learning has demonstrated significant potential and advantages across various fields, especially in multi-view image fusion within complex environments, where its importance is self-evident [1,2]. Multi-view image fusion, which involves the combination of images from different perspectives to obtain more comprehensive and detailed information, is a highly challenging task [3][4][5][6]. In complex environments, the considerable visual disparities between images from different perspectives render the fusion process difficult.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Application of Deep Learning in Multi-View Image Fusion in Complex Environments

Luo,

Shao

2023

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The advancement of technology has unveiled the immense potential of deep learning across various domains, notably in multi-view image fusion within complex environments. Multiview image fusion aims to merge images from different perspectives to garner more comprehensive and detailed information. Despite this, challenges persist in such fusion under complex conditions, particularly when confronting significant variations in perspective and intricate lighting scenarios. Predominant deep learning approaches, reliant on extensive annotated data, grapple with high computational complexity when processing large-scale and high-dimensional image data, thus hindering real-time applicability. This exploration primarily focuses on two facets: multi-view image registration based on the moment of inertia axis method, and multi-view image fusion utilizing morphological decomposition and attention feature integration. The objective is to enhance the efficiency and effectiveness of multi-view image fusion in complex settings, propelling the practical advancement of deep learning technologies.

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Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

Cited by 8 publications

References 49 publications

An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network

Exploring the Application of Deep Learning in Multi-View Image Fusion in Complex Environments

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