Comparative performance analysis of various wavelet and nonlocal means based approaches for image denoising

Singh, Karamjeet; Ranade, Sukhjeet K.; Singh, Chandan Deep

doi:10.1016/j.ijleo.2016.11.055

Cited by 22 publications

(17 citation statements)

References 26 publications

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“…The traditional Bayes shrink threshold T B is defined as follows

T_{B} = \frac{{normalδ}^{2}}{{normalδ}_{x}^{2}}

where

{normalδ}^{2}

and

{normalδ}_{x}^{2}

are the noise variance and signal variance, respectively. The formulas are defined as

δ = \frac{m e d i a n | x_{i, j} true(l, k true) |}{0.6745}

{normalδ}_{x} = \sqrt{\max true(\frac{1}{m n} \sum_{i = 1}^{m} \sum_{j = 1}^{n} x_{i, j}^{2} true(l, k true) - {normalδ}^{2}, 0 true)}

where m and n represent the image, which has the size of m × n , and x i , j ( l , k ) is the coefficient at ( i , j ) in the l th level and k th direction.…”

Section: Methodsmentioning

confidence: 99%

A novel brain image enhancement method based on nonsubsampled contourlet transform

Jia

2017

Int J Imaging Syst Tech

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In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low‐frequency component and several high‐frequency components by the NSCT; Second, the gamma correction is applied to deal with the low‐frequency sub‐band coefficients, and the adaptive threshold is used to remove the noise of the high‐frequency sub‐bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state‐of‐the‐art algorithms in terms of brain image enhancement.

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“…The traditional Bayes shrink threshold T B is defined as follows

T_{B} = \frac{{normalδ}^{2}}{{normalδ}_{x}^{2}}

where

{normalδ}^{2}

and

{normalδ}_{x}^{2}

are the noise variance and signal variance, respectively. The formulas are defined as

δ = \frac{m e d i a n | x_{i, j} true(l, k true) |}{0.6745}

{normalδ}_{x} = \sqrt{\max true(\frac{1}{m n} \sum_{i = 1}^{m} \sum_{j = 1}^{n} x_{i, j}^{2} true(l, k true) - {normalδ}^{2}, 0 true)}

where m and n represent the image, which has the size of m × n , and x i , j ( l , k ) is the coefficient at ( i , j ) in the l th level and k th direction.…”

Section: Methodsmentioning

confidence: 99%

A novel brain image enhancement method based on nonsubsampled contourlet transform

Jia

2017

Int J Imaging Syst Tech

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“…Therefore, it is necessary to reduce the noise and filter the acquired images for subsequent processing and image accuracy. Because of the substantial saltand-pepper noise in SEM images, a median filter is employed in this study to effectively eliminate the noise (Erkan et al 2018;Singh et al 2017;Vijaykumar et al 2014). (2) Image enhancement: In cases of insufficient exposure or excessive exposure, the gray levels in an image can be limited, causing problems in pore recognition and analysis.…”

Section: Digital Image Processingmentioning

confidence: 99%

Two-dimensional SEM image-based analysis of coal porosity and its pore structure

Zou

She

Peng³

et al. 2020

Int J Coal Sci Technol

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A quantitative analysis of the porosity, pore size distribution, and fractal dimensions of pores is significant for studying the pore structure characteristics of coal. This study utilized 12 anthracite coal samples from the Sihe mining area to explore the pore structure characteristics of the coal therein. Hundred randomly selected points on each sliced coal sample were imaged via scanning electron microscopy, and a total of 1200 images were used for the analysis. The porosity and fractal dimensions of the coal samples were analyzed via digital image processing and box-counting dimension methods. This method is characterized by extensive graphical analysis, and the results are based on statistical methods. These were also used to analyze the structural and development characteristics of the microscopic pores in the coal. The results reveal that the surface porosity obtained via digital image processing was 16.11% lower than that measured experimentally. The fractal dimension and porosity of the pore surface were fitted to a natural logarithmic curve. The rate of change in the pore fractal dimension depends on the porosity such that, to some degree, a greater porosity is associated with more complex pore structures, a higher degree of micropore development, and improved pore connectivity.

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“…Many denoising methods for MRI have been proposed in the literature, these methods can be divided into three major classes [ 11 , 12 ]: (1) filtering techniques include linear filters (i.e., spatial and temporal methods) and non-linear filters (i.e., anisotropic diffusion filtering (ADF) -based methods) [ 10 ], 4th order partial differential equation (PDE) –based methods, non-local means (NLM) –based methods [ 13 ] and combination of domain and range filters (i.e., bilateral and trilateral filters); (2) transform domain methods, this class consider the curvelet and the contourlet transforms [ 14 , 15 ] and the wavelet transform based methods (i.e., wavelet thresholding, wavelet domain filter, wavelet packet analysis, adaptive multiscale product thresholding, multiwavelet and undecimated wavelet) [ 7 , 12 , 16 ]; (3) Statistical methods such as maximum likelihood estimation approach [ 17 ], Bayesian approach [ 18 ], linear minimum mean square error estimation approach, phase error estimation approach, nonparametric neighborhood statistics/estimation approach and singularity function analysis [ 11 , 18 , 19 ]. Additionally, there exist some hybrid methodologies that belong to both NLM-based methods and Statistical approaches [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

Orea-Flores

Gallegos‐Funes

Arellano-Reynoso

2019

Entropy

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In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.

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Comparative performance analysis of various wavelet and nonlocal means based approaches for image denoising

Cited by 22 publications

References 26 publications

A novel brain image enhancement method based on nonsubsampled contourlet transform

A novel brain image enhancement method based on nonsubsampled contourlet transform

Two-dimensional SEM image-based analysis of coal porosity and its pore structure

Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

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