scholarly journals Kernel Based Telegraph-Diffusion Equation for Image Noise Removal

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Yu-Qian Yang ◽  
Cheng-Yi Zhang
Keyword(s):  
Partial Differential Equations ◽  
Diffusion Equation ◽  
Anisotropic Diffusion ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Second Order ◽  
Challenging Problem ◽  
Signal To Noise ◽  
Edge Preservation ◽  
Noise Smoothing

The second-order partial differential equations have good performances on noise smoothing and edge preservation. However, for low signal-to-noise ratio (SNR) images, the discrimination between edges and noise is a challenging problem. In this paper, the authors propose a kernel based telegraph-diffusion equation (KTDE) for noise removal. In this method, a kernelized gradient operator is introduced in the second-order telegraph-diffusion equation (TDE), which leads to more effective noise removal capability. Experiment results show that this method outperforms several anisotropic diffusion methods and the TDE method for noise removal and edge preservation.

A NOVEL NOISE REMOVAL METHOD BASED ON FRACTIONAL ANISOTROPIC DIFFUSION AND SUBPIXEL APPROACH

2011 ◽  
Vol 07 (01) ◽  
pp. 173-185 ◽  
Author(s):  
YINGTAO ZHANG ◽  
H. D. CHENG ◽  
YANGQUAN CHEN ◽  
JIANHUA HUANG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Efficient Method ◽  
Anisotropic Diffusion ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Nonlinear Pde ◽  
Signal To Noise ◽  
Partial Difference ◽  
Better Than

Partial differential equations (PDE) have been successfully and widely applied to image processing and computer vision. Anisotropic diffusion is an approach to remove noise based on nonlinear PDE. Many anisotropic methods have been studied; however, they suffer two major drawbacks: blurring and staircasing effects degrading the performance of noise removal filters. To overcome such problems, in this paper, a novel and efficient method for image denoising based on fractional-order anisotropic diffusion and subpixel approach is proposed. Numerical computation is implemented by using the subpixel fractional partial difference (SFPD) approach to increase the flexibility and accuracy. The experimental results demonstrate that the proposed approach can achieve higher signal-to-noise ratio (SNR) and its performance is much better than that of the existing filters.

scholarly journals Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5202 ◽  
Author(s):  
Yu ◽  
Ji ◽  
Xue ◽  
Wang
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Gaussian Noise ◽  
Quotient Space ◽  
Signal To Noise Ratio ◽  
Phase Error ◽  
Phase Shifting ◽  
Signal To Noise ◽  
Partial Differential ◽  
Filtering Method

Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

Improved Algorithm for Wavelet Threshold Denoising and Application in Crack Image Recognition

2014 ◽  
Vol 945-949 ◽  
pp. 1846-1850
Author(s):  
Hai Biao Li ◽  
Xin Xia
Keyword(s):  
Image Recognition ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Threshold Value ◽  
Image Noise ◽  
Threshold Function ◽  
Determination Method ◽  
Signal To Noise ◽  
Visual Effects ◽  
Edge Preservation

In crack image recognition, Donoho’s universal wavelet threshold de-noising method appears "over-kill" phenomenon due to the lack of self-adaptability of threshold value; hence the image may lose its edge details. To handle this problem, the Donoho’s universal threshold and threshold function are improved and an adaptive determination method of threshold coefficient is introduced in this paper. Experimental results shows that the proposed method can effectively remove digital image noise and achieve a better edge protection, higher edge preservation index, better visual effects and higher peak signal-to-noise ratio.

scholarly journals Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Keya Huang ◽  
Hairong Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Visual Quality ◽  
Noise Removal ◽  
Interactive Systems ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Adaptive Median Filter ◽  
Mixed Noise ◽  
Filtering Effect

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.

scholarly journals Pendekatan Numerik pada model Isotropic dan Anisotropic diffusion dalam Pengolahan Citra

2016 ◽  
Vol 1 (2) ◽  
pp. 83 ◽  
Author(s):  
Iryanto , ◽  
Friska Fristella ◽  
Putu Harry Gunawan
Keyword(s):  
Anisotropic Diffusion ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Isotropic Diffusion ◽  
Noise Ratio

<p>Tujuan dari penulisan ini adalah untuk mempelajari model anisotropic diffusion dan persamaan panas isotropic diffusion pada pengolahan citra. Model anisotropic diffusion disebut sebagai persamaan Perona-Malik yang memiliki berbagai fungsi fluks dalam persamaannya. Untuk menghampiri solusi persamaan anisotropic dan isotropic diffusion secara numerik, metode beda hingga digunakan dalam mendiskritkan domain spasial dan waktu dari model yang digunakan. Hasil numerik menggunakan model  anisotropic diffusion menghasilkan gambar yang lebih tajam dibandingkan menggunakan model persamaan panas, dalam hal untuk mempertahankan garis tepi pada citra. Citra yang digunakan dalam penelitian ini adalah citra MRI dan Lena. Hasil pengukuran menggunakan profil histogram dan  Peak Signal to Noise Ratio (PSNR) untuk melihat perbedaan masing-masing hasil simualsi numerik. PSNR pada citra MRI dengan menggunakan model  isotropic  dan anisotropic diffusion berturut-turut didapatkan sebesar 6.1745 dB, dan 6.1833 dB.</p>

Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage

2020 ◽  
Vol 17 (4) ◽  
pp. 1818-1825
Author(s):  
S. Josephine ◽  
S. Murugan
Keyword(s):  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Noise Removal ◽  
Wavelet Thresholding ◽  
Signal To Noise ◽  
Pixel Intensity ◽  
Noisy Pixel ◽  
Shrinkage Method ◽  
Bayesian Shrinkage ◽  
Noise Ratio

In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.

TRAVELING SIGNAL‐TO‐NOISE RATIO AND SIGNAL POWER ESTIMATES

Geophysics ◽  
1967 ◽  
Vol 32 (3) ◽  
pp. 485-493 ◽  
Author(s):  
S. M. Simpson
Keyword(s):  
Seismic Noise ◽  
Signal To Noise Ratio ◽  
Signal Strength ◽  
Noise Removal ◽  
Statistical Properties ◽  
Signal Power ◽  
Signal To Noise ◽  
Direct Estimation ◽  
Noise Ratio

Undesirable seismic noise of a nondeterministic type must be destroyed by making use of its statistical properties. Averaging of one sort or another provides methods for performing this noise removal. Our purpose here is to present a method for direct estimation of signal strength versus seismogram time, with stepout as a parameter. After describing the method and its expected behavior to some extent, we illustrate its application to a set of three noisy records.

Adaptation of Telegraph Diffusion Equation for Noise Reduction on Images

2017 ◽  
Vol 17 (02) ◽  
pp. 1750010 ◽  
Author(s):  
Pandry Koffi Ghislain ◽  
Georges Lausanne Loum ◽  
Ouattara Nouho
Keyword(s):  
Diffusion Equation ◽  
Noise Reduction ◽  
Local Structure ◽  
Signal To Noise Ratio ◽  
Input Image ◽  
Weak Signal ◽  
Signal To Noise ◽  
Reduction Processes ◽  
Fine Structures

The Telegraph Diffusion Equation (TDE) used in some noise reduction processes in an image includes two main parameters: the damping coefficient and the relaxation time. Classically, the first is determined globally for a given input image, while the second one is set constant. In this paper, we propose to determine the values of these parameters according to the information and the image local structure. We then get an adaptive diffusion equation that permits to better control the degree of smoothness and preserve fine structures and image contours avoiding speckles phenomena and staircase. The acquired results show that the proposed method improves the quality of images that have a weak signal-to-noise ratio, comparatively to the methods based on the TDE whose parameters are not adaptive.

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