Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction

doi:10.1109/TCYB.2023.3251730

Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction

2024-05-01

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DOI：10.1109/TCYB.2023.3251730

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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.

Keywords:

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)

Multiple Complementary Priors for Multispectral Image Compressive Sensing Reconstruction

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