Adaptive image sequence resolution enhancement using multiscale-decomposition-based image fusion

2000 ◽  
Author(s):  
Jeong-Ho Shin ◽  
Junghoon Jung ◽  
Joon-Ki Paik ◽  
Mongi A. Abidi
Keyword(s):  
Image Fusion ◽  
Resolution Enhancement ◽  
Image Sequence ◽  
Multiscale Decomposition

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.

Restoration and resolution enhancement of a single image from a vibration-distorted image sequence

1999 ◽  
Author(s):  
Adrian Stern ◽  
Elad Kempner ◽  
Avi Shukrun ◽  
Norman S. Kopeika
Keyword(s):  
Resolution Enhancement ◽  
Image Sequence ◽  
Single Image ◽  
Distorted Image

Multi-Modal Medical Image Fusion Using 3-Stage Multiscale Decomposition and PCNN with Adaptive Arguments

2021 ◽  
Author(s):  
Mummadi Gowthami Reddy ◽  
Palagiri Veera Narayana Reddy ◽  
Patil Ramana Reddy
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Technological Development ◽  
Low Frequency ◽  
Fusion Rule ◽  
Multiscale Decomposition ◽  
Frequency Bands ◽  
Coupled Neural Networks ◽  
Medical Image Fusion

In the current era of technological development, medical imaging plays an important role in many applications of medical diagnosis and therapy. In this regard, medical image fusion could be a powerful tool to combine multi-modal images by using image processing techniques. But, conventional approaches failed to provide the effective image quality assessments and robustness of fused image. To overcome these drawbacks, in this work three-stage multiscale decomposition (TSMSD) using pulse-coupled neural networks with adaptive arguments (PCNN-AA) approach is proposed for multi-modal medical image fusion. Initially, nonsubsampled shearlet transform (NSST) is applied onto the source images to decompose them into low frequency and high frequency bands. Then, low frequency bands of both the source images are fused using nonlinear anisotropic filtering with discrete Karhunen–Loeve transform (NLAF-DKLT) methodology. Next, high frequency bands obtained from NSST are fused using PCNN-AA approach. Now, fused low frequency and high frequency bands are reconstructed using NSST reconstruction. Finally, band fusion rule algorithm with pyramid reconstruction is applied to get final fused medical image. Extensive simulation outcome discloses the superiority of proposed TSMSD using PCNN-AA approach as compared to state-of-the-art medical image fusion methods in terms of fusion quality metrics such as entropy (E), mutual information (MI), mean (M), standard deviation (STD), correlation coefficient (CC) and computational complexity.

scholarly journals CT and MRI image fusion based on multiscale decomposition method and hybrid approach

2019 ◽  
Vol 13 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Lihong Chang ◽  
Xiangchu Feng ◽  
Xiaolong Zhu ◽  
Rui Zhang ◽  
Ruiqiang He ◽  
...  
Keyword(s):  
Image Fusion ◽  
Decomposition Method ◽  
Hybrid Approach ◽  
Multiscale Decomposition ◽  
Mri Image ◽  
Ct And Mri

scholarly journals Resolution enhancement for topography measurement of high-dynamic-range surfaces via image fusion

2018 ◽  
Vol 26 (26) ◽  
pp. 34805 ◽  
Author(s):  
Jian Wang ◽  
Rong Su ◽  
Richard Leach ◽  
Wenlong Lu ◽  
Liping Zhou ◽  
...  
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
Resolution Enhancement ◽  
High Dynamic Range ◽  
High Dynamic
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