Interscale Stein's Unbiased Risk Estimate and Intrascale Feature Patches Distance Constraint for Image Denoising

2010 ◽  
Vol E93-A (8) ◽  
pp. 1434-1441
Author(s):  
Qieshi ZHANG ◽  
Sei-ichiro KAMATA ◽  
Alireza AHRARY
Keyword(s):  
Image Denoising ◽  
Risk Estimate ◽  
Distance Constraint ◽  
Unbiased Risk

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

An Adaptive Wavelet Shrinkage and its Application in Image De-Noising

2011 ◽  
Vol 128-129 ◽  
pp. 500-503
Author(s):  
Tian Jie Cao
Keyword(s):  
Linear Function ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Adaptive Method ◽  
Risk Estimate ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Proportional Relation ◽  
Unbiased Risk

In this paper an adaptive method of shrinkage of the wavelet coefficients is presented. In the method, the wavelet coefficients are divided into two classes by a threshold. One class of them with the smaller absolute values at a scale is transformed with a proportional relation，another class with the larger absolute values at the same scale is transformed with a linear function. The threshold and the coefficient in the proportional relation or in the linear function are determined by the principle of minimizing the Stein’s unbiased risk estimate. In the paper, the method of estimation of the threshold and the coefficient is given and the adaptive method of shrinkage of the wavelet coefficients is applied to image denoising. Examples in the paper show that the presented method has an advantage over SureShrink from the point of view of both the Stein’s unbiased risk estimate and the signal-to-noise ratio. In addition, the method takes almost the same computing time as the SureShrink in image denoising.

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.

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

scholarly journals An algorithm for wavelet thresholding based image denoising by representing images in hexagonal lattice

2018 ◽  
Vol 16 (2) ◽  
Author(s):  
Jeevan K. M ◽  
S. Krishnakumar
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Hexagonal Lattice ◽  
Window Size ◽  
Wavelet Thresholding ◽  
Rectangular Grid ◽  
Hexagonal Grid ◽  
Thresholding Method ◽  
Unbiased Risk ◽  
Grid Processing

The existing method of representation for digital images is using square shaped picture elements called pixels in a rectangular grid. Processing based on hexagonal grid is a new approach in image processing. It has various advantages like symmetry, higher angular resolution, consistent connectivity and higher sampling efficiency. Image processing applications like rotation, scaling, edge detection, and compression in hexagonal domain have already been discussed by many researchers. In this paper we propose an image denoising scheme in hexagonal lattice using wavelet thresholding method. For the thresholding of wavelet coefficients, modified NeighShrink thresholding method is applied. In NeighShrink method, sub-optimal universal threshold and identical neighboring window size in all wavelet sub-bands are used. However, in the proposed method, instead of sub-optimal universal threshold, an optimal threshold is determined for every wavelet sub-band by the Stein’s Unbiased Risk Estimate (SURE). Denoising is performed on images represented in rectangular grid as well as hexagonal grid using modified thresholding method for comparison. MSE, PSNR and SSIM are used for the performance analysis. The obtained results confirm that the proposed method gives better results than existing algorithms.

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