The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser
Abstract
Hyperspectral image (HSI) and multispectral image (MSI) fusion, which fuses a low-spatial-resolution HSI (LR-HSI) with a higher resolution multispectral image (MSI), has become a common scheme to obtain high-resolution HSI (HR-HSI). This article presents a novel HSI and MSI fusion method (called as CNN-Fus), which is based on the subspace representation and convolutional neural network (CNN) denoiser, i.e., a well-trained CNN for gray image denoising. Our method only needs to train the CNN on the more accessible gray images and can be directly used for any HSI and MSI data sets without retraining. First, to exploit the high correlations among the spectral bands, we approximate the desired HR-HSI with the low-dimensional subspace multiplied by the coefficients, which can not only speed up the algorithm but also lead to more accurate recovery. Since the spectral information mainly exists in the LR-HSI, we learn the subspace from it via singular value decomposition. Due to the powerful learning performance and high speed of CNN, we use the well-trained CNN for gray image denoising to regularize the estimation of coefficients. Specifically, we plug the CNN denoiser into the alternating direction method of multipliers (ADMM) algorithm to estimate the coefficients. Experiments demonstrate that our method has superior performance over the state-of-the-art fusion methods.
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Cites
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Cited by
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SSR-NET: Spatial–Spectral Reconstruction Network for Hyperspectral and Multispectral Image Fusion
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EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
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Recent advances and new guidelines on hyperspectral and multispectral image fusion
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Self-citation
Metadata
- Authors
- Dian, R., Li, S., & Kang, X.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2021-03
- Volume
- 32
- Issue
- 3
- Pages
- 1124-1135
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3016410830
- Cited by
- 328
- References
- 54
- FWCI
- 25.61
- Type
- journal article
- Language
- en
- Open access
- closed
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