Improving traditional method used for medical image fusion by deep learning approach-based convolution neural network

El‐Shafai, Walid; Ghandour, C.; El‐Rabaie, S.

doi:10.1007/s12596-023-01123-y

Cited by 3 publications

(1 citation statement)

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“…However, despite significant achievements of deep learning in many domains, including image processing, several issues and challenges remain in multi-view image fusion within complex environments. For example, existing deep learning methods often rely on large amounts of annotated data, which are challenging to obtain in practical applications [18][19][20][21]. Additionally, these methods struggle with high computational complexity when processing large-scale and high-dimensional image data, making it difficult to meet real-time requirements.…”

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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Section: Introductionmentioning

confidence: 99%