Fusion of Hyperspectral and Multispectral Images by Convolutional Sparse Representation

Xing, Changda; Wang, Meiling; Cong, Yuhua; Wang, Zhisheng

doi:10.1109/lgrs.2022.3155595

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…[91][92][93][94]. Other techniques employed for the same purpose include subspace learning [95], sparse representations and dictionary learning [96][97][98][99][100], and CNN [101,102].…”

Section: Multimodal Image Fusionmentioning

confidence: 99%

Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components

Veshki

Vorobyov

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…[91][92][93][94]. Other techniques employed for the same purpose include subspace learning [95], sparse representations and dictionary learning [96][97][98][99][100], and CNN [101,102].…”

Section: Multimodal Image Fusionmentioning

confidence: 99%

Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components

Veshki

Vorobyov

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Generally, HSI-MSI fusion approaches can be categorized as three classes, i.e., low-rank based- [7], [8], [9], [10], [11], sparse based- [12], [13], [14], and deep-learning basedmethods [15], [16], [17]. Instead of modeling HSI directly at the pixel level, low-rank based methods usually adopt matrix or tensor decomposition, supplemented by appropriate constraints, to search the potential HR-HSI.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Multispectral Data Fusion via Joint Local-Nonlocal Modeling and Truncation Operator

Jiang

Feng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fusion technology has been the core strategy to obtain a high-spatial-spectral resolution hyperspectral image (HSI). In recent years, few fusion models focused on exploiting the underlying manifold structure in the spatial dimension of the high-resolution HSI. We assume that image patches of highresolution multi-spectral image (HR-MSI) and high-resolution hyperspectral image (HR-HSI) share similar manifold structures. Through this bridge, a local-nonlocal low dimensional manifold defined from HR-MSI is built to favor the patch relationship in HR-HSI, which is further utilized to build a set of orthogonal bases. Subsequently, we introduce a truncation operation based on the adaptively constructed orthogonal bases, which can efficiently preserve the low-frequency information of the image and reduce the interference of noise. Finally, we combine the local-nonlocal manifold term and the truncation operation, coined as LNTM, into a variational super-resolution framework to regularize the latent HR-HSI, leading to a strongly convex function regarding the fusion problem. The cost function is solved by a carefully designed variant of alternating direction method of multipliers (ADMM) algorithm. Different experiments on three public benchmarks validate our algorithm outperforms the recent start-of-the-arts concerning both visual quality and numerical metrics.

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MACCNet: Multiscale Attention and Cross- Convolutional Network for Infrared and Visible Image Fusion

Yang,

Zhou,

Wan

et al. 2024

IEEE Sensors J.

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Fusion of Hyperspectral and Multispectral Images by Convolutional Sparse Representation

Cited by 4 publications

References 26 publications

Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components

Efficient Online Convolutional Dictionary Learning Using Approximate Sparse Components

Hyperspectral and Multispectral Data Fusion via Joint Local-Nonlocal Modeling and Truncation Operator

MACCNet: Multiscale Attention and Cross- Convolutional Network for Infrared and Visible Image Fusion

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