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Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction
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AI
DOI：10.1109/TCYB.2023.3251730.png)
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摘要
En 中文
Compressive sensing (CS) techniques using a few compressed measurements have drawn considerable interest in reconstructing multispectral imagery (MSI). Nonlocal-based tensor methods have been widely used for MSI-CS reconstruction, which employ the nonlocal self-similarity (NSS) property of MSI to obtain satisfactory results. However, such methods only consider the internal priors of MSI while ignoring important external image information, for example deep-driven priors learned from a corpus of natural image datasets. Meanwhile, they usually suffer from annoying ringing artifacts due to the aggregation of overlapping patches. In this article, we propose a novel approach for highly effective MSI-CS reconstruction using multiple complementary priors (MCPs). The proposed MCP jointly exploits nonlocal low-rank and deep image priors under a hybrid plug-and-play framework, which contains multiple pairs of complementary priors, namely, internal and external, shallow and deep, and NSS and local spatial priors. To make the optimization tractable, a well-known alternating direction method of multiplier (ADMM) algorithm based on the alternating minimization framework is developed to solve the proposed MCP-based MSI-CS reconstruction problem. Extensive experimental results demonstrate that the proposed MCP algorithm outperforms many state-of-the-art CS techniques in MSI reconstruction. The source code of the proposed MCP-based MSI-CS reconstruction algorithm is available at: https://github.com/zhazhiyuan/MCP_MSI_CS_Demo.git.
Keyword:
Image reconstruction
Tensors
Optimization
Minimization
Light rail systems
Dictionaries
Correlation
Alternating direction method of multiplier (ADMM)
alternating minimization
Index Terms
compressive sensing (CS)
deep image (DI) prior
hybrid plug-and-play (H-PnP)
low-rank
multispectral imagery (MSI) reconstruction
nonlocal self-similarity (NSS)
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
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Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition基于全变分正则化和非局部低秩张量分解的高光谱图像去噪
PREAI
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