scholarly journals IR and VI Fusion using Hybrid VSM and Swarm Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 2514-2521
Keyword(s):  
Image Fusion ◽  
Real World ◽  
Wavelet Transforms ◽  
Quality Measures ◽  
Directional Selectivity ◽  
Swarm Optimization ◽  
Directional Information ◽  
Person Detection ◽  
Ir Sensors ◽  
Fusion Methods

In the current era, usage of Infrared (IR) sensors, in the real world has been increased because of decrease in their cost. Many recent applications of IR images are included in the field of defence, medicine, astronomy, meteorology, industry and science. One of the main applications in the field of defence is person detection. However, IR images have unique challenges. Image fusion based on DWT superior things because it is a multiresolution approach, it allows picture disintegration in various parameters and provides directional information. Most of the existing methods used two or three different quality measures to measure the performance of the fused system. The present study used the ten quality metrics on all derived approaches and noted down various qualitative conclusions. Proposed system the base layers obtained and Further the proposed work found that, the methods based on wavelet transforms have compactness, directional selectivity and orthogonality. Due to these, DWT based fusion methods are used in literature when compared to pyramid decomposition based fusion methods

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

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.

scholarly journals Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

2020 ◽  
Vol 12 (6) ◽  
pp. 1009
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Qimin Cheng ◽  
Xiaoyi Long ◽  
Yuxin Yuan
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Information ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Remote Sensing Image Fusion ◽  
Fusion Methods ◽  
Invariant Regions ◽  
The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

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