scholarly journals An Improved Pulse-Coupled Neural Network Model for Pansharpening

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2764 ◽  
Author(s):  
Xiaojun Li ◽  
Haowen Yan ◽  
Weiying Xie ◽  
Lu Kang ◽  
Yi Tian
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Multispectral Image ◽  
Transform Domain ◽  
Human Visual Perception ◽  
Pulse Coupled Neural Network ◽  
Proposed Model ◽  
Spectral Fidelity ◽  
Fusion Methods ◽  
Injection Mechanism

Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

scholarly journals Coupled Convolutional Neural Network-Based Detail Injection Method for Hyperspectral and Multispectral Image Fusion

2020 ◽  
Vol 11 (1) ◽  
pp. 288
Author(s):  
Xiaochen Lu ◽  
Dezheng Yang ◽  
Fengde Jia ◽  
Yifeng Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
State Of The Art ◽  
Spectral Feature ◽  
Injection Method ◽  
Feature Extraction Method ◽  
Injection Model ◽  
Spectral Fidelity ◽  
Fusion Methods

In this paper, a detail-injection method based on a coupled convolutional neural network (CNN) is proposed for hyperspectral (HS) and multispectral (MS) image fusion with the goal of enhancing the spatial resolution of HS images. Owing to the excellent performance in spectral fidelity of the detail-injection model and the image spatial–spectral feature exploration ability of CNN, the proposed method utilizes a couple of CNN networks as the feature extraction method and learns details from the HS and MS images individually. By appending an additional convolutional layer, both the extracted features of two images are concatenated to predict the missing details of the anticipated HS image. Experiments on simulated and real HS and MS data show that compared with some state-of-the-art HS and MS image fusion methods, our proposed method achieves better fusion results, provides excellent spectrum preservation ability, and is easy to implement.

scholarly journals Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner

2021 ◽  
Vol 13 (16) ◽  
pp. 3226
Author(s):  
Jianhao Gao ◽  
Jie Li ◽  
Menghui Jiang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Image Fusion ◽  
Multispectral Image ◽  
Hyperspectral Images ◽  
Training Dataset ◽  
Diffusion Method ◽  
Simple Diffusion ◽  
Fusion Methods

Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends on the size and quality of training dataset, constraining the application of deep learning under the situation where training dataset is not available or of low quality. In this paper, we introduce a novel fusion method, which operates in a self-supervised manner, to the task of hyperspectral and multispectral image fusion without training datasets. Our method proposes two constraints constructed by low-resolution hyperspectral images and fake high-resolution hyperspectral images obtained from a simple diffusion method. Several simulation and real-data experiments are conducted with several popular remote sensing hyperspectral data under the condition where training datasets are unavailable. Quantitative and qualitative results indicate that the proposed method outperforms those traditional methods by a large extent.

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.

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