Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty

Wang Shuo; Zhu Zhibin; Zhao RuwenZhang Benxin

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Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty

机译：Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty

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Hyperspectral images (HSIs) can help deliver more reliable representations of real scenes than traditional images and enhance the performance of many computer vision tasks. However, in real cases, an HSI is often degraded by a mixture of various types of noise, including Gaussian noise and impulse noise. In this paper, we propose a logarithmic nonconvex regularization model for HSI mixed noise removal. The logarithmic penalty function can approximate the tensor fibered rank more accurately and treats singular values differently. An alternating direction method of multipliers (ADMM) is also presented to solve the optimization problem, and each subproblem within ADMM is proven to have a closed-form solution. The experimental results demonstrate the effectiveness of the proposed method.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2021年第29期|5535169.1-5535169.18|共18页
作者
Wang Shuo; Zhu Zhibin; Zhao RuwenZhang Benxin;
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Guilin Univ Elect Technol, Sch Elect Engn & Automat, Key Lab Automat Detecting Technol & Instruments, Guilin 541004, Peoples R China;

Guilin Univ Elect Technol, Guangxi Coll & Univ Key Lab Data Anal & Computat, Sch Math & Comp Sci, Guilin 541004, Peoples R China;

Guilin Univ Elect Technol, Sch Elect Engn & Automat, Key Lab Automat Detecting Technol & Instruments, Guilin 541004, Peoples R China|Guilin Univ Elect Technol, Guangxi Coll & Univ Key Lab Data Anal & Computat, Sch Math & Comp Sci, Guilin 541004, Peoples R;

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Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty

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