Image Fusion based on Convolutional Neural Network using OpenCV, Python
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Satellite Image Analysis Using Convolutional Neural Network
Multimodal Satellite image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents multimodal fusion framework using the non sub-sampled Contour let transform (NSCT) domains for images acquired using two distinct Hyper Spectral and Multi Spectral Images. The major advantage of using NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a NSCT domain for fusion and then second stage to enhance the contrast of the diagnostic features by using Guided filter. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.
- Image averaging and maximization method
- Principal component analysis
- Discrete Cosine Transform
- Multimodal medical image fusion based on Non subsample contour let transform
- Guided Filter
- NSCT based Multistage decomposition
- Pixel Level Fusion
- Guided Filter
- Performance Analysis
- Satellite Application
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