scholarly journals Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shuo Wang ◽  
Zhibin Zhu ◽  
Ruwen Zhao ◽  
Benxin Zhang
Keyword(s):  
Optimization Problem ◽  
Hyperspectral Image ◽  
Closed Form Solution ◽  
Singular Values ◽  
Noise Removal ◽  
Form Solution ◽  
Alternating Direction ◽  
Nonconvex Regularization ◽  
Mixed Noise ◽  
Mixed Noise Removal

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.

Mixed Noise Removal in Hyperspectral Image via Low-Fibered-Rank Regularization

2020 ◽  
Vol 58 (1) ◽  
pp. 734-749 ◽  
Author(s):  
Yu-Bang Zheng ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tai-Xiang Jiang ◽  
Tian-Hui Ma ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Noise Removal ◽  
Mixed Noise ◽  
Mixed Noise Removal

A mixed noise removal algorithm based on multi-fidelity modeling with nonsmooth and nonconvex regularization

2019 ◽  
Vol 78 (16) ◽  
pp. 23117-23140 ◽  
Author(s):  
Chun Li ◽  
Yuepeng Li ◽  
Zhicheng Zhao ◽  
Longlong Yu ◽  
Ze Luo
Keyword(s):  
Noise Removal ◽  
Nonconvex Regularization ◽  
Mixed Noise ◽  
Mixed Noise Removal

scholarly journals Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

2021 ◽  
Vol 13 (20) ◽  
pp. 4098
Author(s):  
Lina Zhuang ◽  
Michael K. Ng ◽  
Xiyou Fu
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Hyperspectral Data ◽  
Image Prior ◽  
Noise Impulse ◽  
Mixed Noise ◽  
Deep Cnn ◽  
Mixed Noise Removal ◽  
The Cost

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.

scholarly journals A Novel 3D Anisotropic Total Variation Regularized Low Rank Method for Hyperspectral Image Mixed Denoising

2018 ◽  
Vol 7 (10) ◽  
pp. 412 ◽  
Author(s):  
Le Sun ◽  
Tianming Zhan ◽  
Zebin Wu ◽  
Byeungwoo Jeon
Keyword(s):  
Total Variation ◽  
Hyperspectral Image ◽  
Main Idea ◽  
Noise Removal ◽  
Low Rank ◽  
Superior Performance ◽  
Alternating Direction ◽  
Rank Constraints ◽  
Mixed Noise Removal ◽  
Anisotropic Total Variation

Known to be structured in several patterns at the same time, the prior image of interest is always modeled with the idea of enforcing multiple constraints on unknown signals. For instance, when dealing with a hyperspectral restoration problem, the combination of constraints with piece-wise smoothness and low rank has yielded promising reconstruction results. In this paper, we propose a novel mixed-noise removal method by employing 3D anisotropic total variation and low rank constraints simultaneously for the problem of hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in an HSI lies in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rankness of an HSI is approximately exploited by the nuclear norm, while the spectral-spatial smoothness is explored using 3D anisotropic total variation (3DATV), which is defined as a combination of 2D spatial TV and 1D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by the alternating direction method of multipliers (ADMM). Experimental results of both simulated and real HSI datasets validate the superior performance of the proposed method in terms of quantitative assessment and visual quality.

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