Bayesian dictionary learning for hyperspectral image super resolution in mixed Poisson–Gaussian noise

Zou, Changzhong; Xia, Youshen

doi:10.1016/j.image.2017.09.003

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…In the extended target Shack-Hartmann wavefront sensor for measurement, the transmitted signal is affected and interfered with by a variety of factors, including environmental stray light, device characteristics, detector characteristics, and other factors, which introduces a variety of noise. According to statistical law, its noise distribution includes two types: signal-independent Gaussian noise and signal-dependent Poisson noise [29][30][31]. The noise model equation is represented by Equation (9):…”

Section: Generalized Anscombe Transformmentioning

confidence: 99%

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Chen,

Jia,

Zhou

et al. 2023

Applied Sciences

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Due to the influence of atmospheric turbulence, the detector, and background noise, the subaperture image of an extended scene Shack–Hartmann wavefront sensor will have a low signal-to-noise ratio, which will introduce errors to the offset estimation and reduce the accuracy of the slope measurement. To solve this problem, this paper proposes a cross-correlation subaperture image preprocessing method, which uses the generalized Anscombe transform to convert the Gauss–Poisson noise into Gaussian noise and introduces residual feedback on the basis of BM3D to achieve the efficient denoising of subaperture images. The simulation results show that compared with the three commonly used denoising algorithms, the proposed method improves the relative error of the subaperture offset calculation by 51.96% and the corresponding Zernike coefficient of distorted reconstruction wavefront by 85.56%, which realizes the improvement in the detection accuracy on the basis of effectively retaining image details.

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Section: Generalized Anscombe Transformmentioning

confidence: 99%

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Chen,

Jia,

Zhou

et al. 2023

Applied Sciences

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“…With growing interest in the Poisson -Gaussian noise model, there exist many methods of image denoising of images under Poisson -Gaussian noise, for instance, the simplified noise model [Jeong et al, 2014], Variance Stabilization [Bohra et al, 2019], exact Poisson -Gaussian likelihood [Chouzenoux et al, 2015], blindspot neural network [Khademi et al, 2021], total variation (TV) based methods [Calatroni, De Los Reyes, Schronlieb, 2017], Anscombe transformation [Makitalo, Foi, 2013], dictionary learning [Zou, Xia, 2018] etc.…”

Section: Introductionmentioning

confidence: 99%

Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm

Pham¹,

Tran²,

Dang³

2023

CRM

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In various applications, i. e., astronomical imaging, electron microscopy, and tomography, images are often damaged by Poisson noise. At the same time, the thermal motion leads to Gaussian noise. Therefore, in such applications, the image is usually corrupted by mixed Poisson -Gaussian noise.In this paper, we propose a novel method for recovering images corrupted by mixed Poisson -Gaussian noise. In the proposed method, we develop a total variation-based model connected with the nonconvex function and the total generalized variation regularization, which overcomes the staircase artifacts and maintains neat edges.Numerically, we employ the primal-dual method combined with the classical iteratively reweighted l 1 algorithm to solve our minimization problem. Experimental results are provided to demonstrate the superiority of our proposed model and algorithm for mixed Poisson -Gaussian removal to state-of-the-art numerical methods.

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“…Consequently, speckle noise removal is more complex and more challenging than additive noise removal. The speckle noise is described by different probability density function, includes Gamma, Poisson and Rayleigh distribution functions [17]. Over the past two decades, there are several variational approaches for addressing speckle noise removal problem.…”

Section: Introductionmentioning

confidence: 99%

Speckle Noise Removal Convex Method Using Higher-Order Curvature Variation

Huang

Pan

et al. 2019

IEEE Access

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In order to remove speckle noise while preserving image features, a novel variational model for image restoration based on total curvature is proposed in this paper. Due to the characteristics of nonlinear, non-convex, and non-smooth, the proposed variational model is transformed into an alternating optimization problem through introducing a series of auxiliary variables and using the alternating direction method of multipliers. In each loop of optimization, the Fast Fourier Transform is employed to solve the involved partial differential equations numerically, and the generalized soft thresholding formula is used to solve the involved algebra equations analytically. Moreover, the projection method is applied to fulfill some related inequality constraints simply for different sub-problems, thus the computational efficiency is improved totally. The numerous experiments on synthetic and real cases are implemented to demonstrate the advantages of the proposed model in image edge and corner preserving, and to indicate the high computation efficiency of the designed algorithm through comparison with other fast algorithms. INDEX TERMS Speckle noise, curvature-dependent, alternating direction method of multipliers, discrete Fourier transform.

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Bayesian dictionary learning for hyperspectral image super resolution in mixed Poisson–Gaussian noise

Cited by 16 publications

References 34 publications

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm

Speckle Noise Removal Convex Method Using Higher-Order Curvature Variation

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