Erratum to “An Unbiased Risk Estimator for Image Denoising in the Presence of Mixed Poisson-Gaussian Noise” [Mar 14 1255-1268]

2014 ◽  
Vol 23 (6) ◽  
pp. 2750-2750
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Risk Estimator ◽  
Unbiased Risk

scholarly journals An Unbiased Risk Estimator for Image Denoising in the Presence of Mixed Poisson–Gaussian Noise

2014 ◽  
Vol 23 (3) ◽  
pp. 1255-1268 ◽  
Author(s):  
Yoann Le Montagner ◽  
Elsa D. Angelini ◽  
Jean-Christophe Olivo-Marin
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Risk Estimator ◽  
Unbiased Risk

scholarly journals Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator

2019 ◽  
Vol 28 (10) ◽  
pp. 4899-4911 ◽  
Author(s):  
Caoyuan Li ◽  
Hong-Bo Xie ◽  
Xuhui Fan ◽  
Richard Yi Da Xu ◽  
Sabine Van Huffel ◽  
...  
Keyword(s):  
Image Denoising ◽  
Singular Value ◽  
Risk Estimator ◽  
Unbiased Risk ◽  
Singular Value Thresholding

Minimum unbiased risk estimate based 2DPCA for color image denoising

2021 ◽  
Author(s):  
Mingli Wang ◽  
Xinwei Jiang ◽  
Junbin Gao ◽  
Tianjiang Wang ◽  
Chunlong Hu ◽  
...  
Keyword(s):  
Image Denoising ◽  
Color Image ◽  
Risk Estimate ◽  
Color Image Denoising ◽  
Unbiased Risk

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

2015 ◽  
Vol 14 (02) ◽  
pp. 1550017
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Classical Problem ◽  
Statistical Parameter ◽  
Noise Model ◽  
Natural Image ◽  
Bayesian Techniques ◽  
Generalized Gamma ◽  
Gamma Density

The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

2012 ◽  
pp. 227-235 ◽  
Author(s):  
D. Selvathi ◽  
S. Thamarai Selvi ◽  
C. Loorthu Sahaya Malar
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Diffusion Tensor ◽  
Curvelet Transform ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Image Domain ◽  
Active Research ◽  
Unbiased Risk ◽  
Wavelet Image

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact on the shapes and orientations of tensors in diffusion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.

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