A Comparative Analysis of Image Fusion Methods Using Texture

2013 ◽  
pp. 339-351
Author(s):  
Jharna Majumdar ◽  
Bhuvaneshwari S. Patil
Keyword(s):  
Comparative Analysis ◽  
Image Fusion ◽  
Fusion Methods

A Comparative Analysis of Feature Based Image Fusion Methods

2007 ◽  
Vol 6 (8) ◽  
pp. 1224-1230 ◽  
Author(s):  
M. Sasikala ◽  
N. Kumaravel
Keyword(s):  
Comparative Analysis ◽  
Image Fusion ◽  
Fusion Methods ◽  
Feature Based

Comparative Analysis of Image Fusion Methods in Marine Environment

2019 ◽  
Author(s):  
Fahimeh Farahnakian ◽  
Parisa Movahedi ◽  
Jussi Poikonen ◽  
Eero Lehtonen ◽  
Dimitrios Makris ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Image Fusion ◽  
Marine Environment ◽  
Fusion Methods

A comparative analysis of image fusion methods

2012 ◽  
Author(s):  
Irfan Kosesoy ◽  
Abdulkadir Tepecik ◽  
Mufit Cetin ◽  
Altan Mesut
Keyword(s):  
Comparative Analysis ◽  
Image Fusion ◽  
Fusion Methods

A comparative analysis of image fusion methods

2005 ◽  
Vol 43 (6) ◽  
pp. 1391-1402 ◽  
Author(s):  
Zhijun Wang ◽  
D. Ziou ◽  
C. Armenakis ◽  
D. Li ◽  
Qingquan Li
Keyword(s):  
Comparative Analysis ◽  
Image Fusion ◽  
Fusion Methods

scholarly journals Infrared and visible image fusion via octave Gaussian pyramid framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Yan ◽  
Qun Hao ◽  
Jie Cao ◽  
Rizvi Saad ◽  
Kun Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Visible Image ◽  
Composite Image ◽  
Multiscale Decomposition ◽  
Gaussian Pyramid ◽  
Fusion Methods ◽  
Scale Spaces ◽  
Fusion Framework

AbstractImage fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

Computed tomography image denoising based on multi dose CT image fusion and extended sparse techniques

2021 ◽  
Author(s):  
Anuyogam Venkataraman
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Region Of Interest ◽  
Source Image ◽  
High Dose ◽  
Fusion Algorithm ◽  
Quantitative Metrics ◽  
Fused Image ◽  
Fusion Methods ◽  
Computed Tomography Image

With the increasing utilization of X-ray Computed Tomography (CT) in medical diagnosis, obtaining higher quality image with lower exposure to radiation is a highly challenging task in image processing. Sparse representation based image fusion is one of the sought after fusion techniques among the current researchers. A novel image fusion algorithm based on focused vector detection is proposed in this thesis. Firstly, the initial fused vector is acquired by combining common and innovative sparse components of multi-dosage ensemble using Joint Sparse PCA fusion method utilizing an overcomplete dictionary trained using high dose images of the same region of interest from different patients. And then, the strongly focused vector is obtained by determining the pixels of low dose and medium dose vectors which have high similarity with the pixels of the initial fused vector using certain quantitative metrics. Final fused image is obtained by denoising and simultaneously integrating the strongly focused vector, initial fused vector and source image vectors in joint sparse domain thereby preserving the edges and other critical information needed for diagnosis. This thesis demonstrates the effectiveness of the proposed algorithms when experimented on different images and the qualitative and quantitative results are compared with some of the widely used image fusion methods.

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