scholarly journals Location Related Signals with Satellite Image Fusion Method Using Visual Image Integration Method

2020 ◽  
Vol 35 (5) ◽  
pp. 385-393
Author(s):  
G. Ravikanth ◽  
K. V. N. Sunitha ◽  
B. Eswara Reddy
Keyword(s):  
Image Fusion ◽  
Integration Method ◽  
Satellite Image ◽  
Visual Image ◽  
Fusion Method ◽  
Image Integration ◽  
Image Fusion Method

scholarly journals Improving the Spatial Resolution of Real Time Satellite Image Fusion Using 2D Curvelet Transform

2018 ◽  
Vol 7 (2.19) ◽  
pp. 55
Author(s):  
Gandla Maharnisha ◽  
R Veerasundari ◽  
Gandla Roopesh Kumar ◽  
Arunraj .
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Median Filter ◽  
Satellite Image ◽  
Low Frequency ◽  
Curvelet Transform ◽  
Principal Component ◽  
Fusion Method ◽  
Spectral Degradation ◽  
Image Fusion Method

The fused image will have structural details of the higher spatial resolution panchromatic images as well as rich spectral information from the multispectral images. Before fusion, Mean adjustment algorithm of Adaptive Median Filter (AMF) and Hybrid Enhancer (combination of AMF and Contrast Limited Adaptive Histogram Equalization (CLAHE)) are used in the pre-processing. Here, conventional Principal Component image fusion method will be compared with newly modified Curvelet transform image fusion method. Principal Component fusion technique will improve the spatial resolution but it may produce spectral degradation in the output image. To overcome the spectral degradation, Curvelet transform fusion methods can be used. Curvelet transform uses curve which represents edges and extraction of the detailed information from the image. Curvelet Transform of individual acquired low-frequency approximate component of PAN image and high-frequency detail components from PAN and MS image is used. Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) are measured to evaluate the image fusion accuracy. 

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.

Gradient-based multi-focus image fusion method using convolution neural network

2021 ◽  
Vol 92 ◽  
pp. 107174
Author(s):  
Yang Zhou ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Kai Liu ◽  
Marco Anisetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Convolution Neural Network ◽  
Fusion Method ◽  
Gradient Based ◽  
Focus Image ◽  
Image Fusion Method
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