Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty

YUAN Yuli; LYU Junrui; LUO Xuegang

doi:10.5768/JAO202142.0202005

Journal of Applied Optics > 2021 > 42(2): 283-291. > DOI: 10.5768/JAO202142.0202005

YUAN Yuli, LYU Junrui, LUO Xuegang. Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty[J]. Journal of Applied Optics, 2021, 42(2): 283-291. DOI: 10.5768/JAO202142.0202005

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Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty

YUAN Yuli^1,,
LYU Junrui²,
LUO Xuegang^{2, 3, 4}

1.
College of Computer Science，Neijiang Normal College，Neijiang 641100，China
2.
School of Mathematics and Computer Science，Panzhihua University，Panzhihua 617000，China
3.
School of Information and Engineering,Sichuan Tourism University, Chengdu 610100, China
4.
School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

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Received Date: August 06, 2020
Revised Date: November 08, 2020
Available Online: February 23, 2021

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Abstract

Abstract

To solve the loss of image details when removing the stripe noise of hyperspectral images (HSI), a destriping method for hyperspectral images was proposed, which was based on the weighted block sparsity (WBS) regularization and the minimax concave penalty (MCP) constraint (WBS-MCP). WBS-MCP was constructed by employing weighted ℓ_2,1norm and MCP norm for the sparse features and low-rank constraints, and applying ℓ₁ norm to the edge smoothing constraint of clean image. The alternating direction method of multipliers (ADMM) algorithm was used to iteratively solve the corresponding constraint model, and the clean HSI image was obtained by reconstruction. The experimental results show that the mean equivalent number of look of real HSI improves from 28.45 to 83.47, and the edge retention index increased by 0.056 at least, especially for the aperiodic stripe noise. The adaptive weight was used to update the sparse level, which could enhance the group sparsity and has better performance in maintaining the image edge and enhancing the area smoothness, so that the noise removal has better effect on aperiodic stripe noise.
- weighted block sparsity,
- minimax concave penalty,
- hyperspectral images stripe noise

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References (18)

References

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Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty