scholarly journals Performance Analysis of Different Restoration Techniques for Degraded 3D Cervical Spine Mri Image

2021 ◽  
Vol 14 (02) ◽  
pp. 853-867
Author(s):  
Neelam Turk ◽  
Sangeeta Kamboj ◽  
Sonam Khera ◽  
Neha Rajput
Keyword(s):  
Performance Analysis ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Linear Filters ◽  
Restoration Technique ◽  
Restoration Techniques ◽  
Accuracy Of Results ◽  
Noise Models ◽  
Mri Image

In the proposed work, noise models such as Salt & Pepper, Gaussian and Poisson are considered in order to corrupt the image.Image restoration is still challenging task to recover an original image using a degradation and restoration model. In the paper, Gaussian, Average and Wiener linear image restoration techniques are used to recover the original MRI image. Median filter, Min and Max non-linear filters are also used to obtain uncorrupt image in the paper.Mean square error (MSE), Peak Signal to Noise Ratio (PSNR) and Cross Correlation(CC) performance analysis criteria are used to compare different restoration technique so that better performance in a clinical diagnosis can be achieved. In the paper it can be found that wiener filter with 5 x 5 window for Gaussian, speckle and Poisson noise provides best performance in terms of MSE and PSNR. Also,median filter with 5 x 5 window gives better accuracy of results to restore 3D salt & pepper noised image in terms of MSE and CC.

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

Performance Analysis of Several Common Filter

2012 ◽  
Vol 220-223 ◽  
pp. 1446-1449
Author(s):  
Hai Bo Jiang ◽  
Jing Zhi Cai
Keyword(s):  
Image Processing ◽  
Kalman Filter ◽  
Performance Indicators ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Paper Analysis ◽  
Signal To Noise ◽  
Initial Stage ◽  
Average Filter

Denoising is the initial stage of image processing, in preparation for the subsequent processing of the image. This article describes a field of several denoising used filters include average filter, median filter, Wiener filter, Kalman filter. Combination of diagrams, will describe their filtering principle, at the end of this paper,analysis signal to noise ratio of image and other performance indicators .

scholarly journals Universal Impulse Noise Suppression Using Extended Efficient Nonparametric Switching Median Filter

2018 ◽  
Vol 214 ◽  
pp. 01003
Author(s):  
M. H. Suid ◽  
M. A. Ahmad ◽  
M. I. F. M. Hanif ◽  
M. Z. Tumari ◽  
M. S. Saealal
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Median Filter ◽  
Noise Model ◽  
Restoration Technique ◽  
Switching Median Filter ◽  
Noise Models ◽  
Impulse Detector ◽  
Better Than

This paper presents a filtering algorithm called extended efficient nonparametric switching median (EENPSM) filter. The proposed filter is composed of a nonparametric easy to implement impulse noise detector and a recursive pixel restoration technique. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to the existing conventional filters that only focus on a particular impulse noise model, the EENPSM filter is capable of filtering all kinds of impulse noise (i.e. the random-valued and/or fixed-valued impulse noise models). Extensive qualitative and quantitative evaluations have shown that the EENPSM method performs better than some of the existing methods by giving better filtering performance.

scholarly journals Removal of various Noises in Dental X-ray Images using Selective Median Filter

2019 ◽  
Vol 8 (12) ◽  
pp. 2550-2558
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Medical Image Analysis ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Linear Filters ◽  
Severe Problem ◽  
X Ray ◽  
Non Linear

Image processing plays major role to provide additional information in medical diagnosis. Input images contain picture information as well as noise information. Noise information is added with the images during signal acquisition stage or in the transmission of image data. Salt & Pepper noise, Gaussian noise and Speckle noise is the major noises introduced in the images. Noise information may be interpreted as data and it may lead to severe problem. Linear and Non-linear filters are used to reduce these noises in the images. In medical image analysis, non-linear filters are preferred over linear filters because it preserves edge information. Dental X-ray image is used to identify the cavities and its depth. Average filter, median filter and wiener filter are the classical techniques used in many image processing applications. In this paper, three different noises (Salt &pepper, Gaussian and Speckle noise) are added and different filters (Average filters, median filter, Wiener filter) performances are analysed with the PSNR, SNR and MSE. Analysis shows that median filter is suitable for reducing salt & pepper noise and wiener filter is suitable for reducing Gaussian noise and speckle noise in the dental x-ray images. Selective median filter is a modified wiener filter. Median filter is used for the pixel value 0 and 255.For other pixel values wiener filter is used. Selective median filter is giving better result than traditional techniques

PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY

2021 ◽  
Vol 12 (1) ◽  
pp. 1-10
Author(s):  
Anshika Jain ◽  
◽  
Maya Ingle
Keyword(s):  
Comparative Study ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Removal ◽  
Poisson Noise ◽  
Signal To Noise ◽  
Mean Filter ◽  
Facial Images

Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.

scholarly journals Perbandingan Algoritma Mean Filter, Median Filter dan Wiener Filter pada Aplikasi Restorasi Citra RGB Terdegradasi Impulse Noise Menggunakan The Peak Signal To Noise Ratio (PSNR)

2017 ◽  
Author(s):  
Arnes Sembiring
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Signal To Noise ◽  
Mean Filter ◽  
Noise Ratio

Artikel ini merupakan versi postprint, artikel ini sudah dipublikasikan pada Jurnal Saintek Fak. Teknik Universitas Islam Sumatera Utara (UISU), ISSN: 2355-2395, Volume 2 Nomor 2 tahun 2015, halaman 234-244

scholarly journals Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains

2012 ◽  
Vol 3 (1) ◽  
pp. 162-166
Author(s):  
Amardeep Singh Virk ◽  
Mandeep Kaur ◽  
Lovely Passrija
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Spatial Filter ◽  
Ultrasound Images ◽  
Filter Methods ◽  
Significant Research ◽  
Wavelet Methods

Denoising is one of the important tasks in image processing. Despite the significant research conducted on this topic, the development of efficient denoising methods is still a compelling challenge. In this paper, spatial domain methods and Wavelet Domain Methods of image denoising have been evaluated. The medical ultrasound images suffer from speckle noise which is multiplicative in nature and more difficult to remove than additive noise. In the spatial filter methods Median Filter and Wiener Filter are implemented. These methods are based on the simple formulas that are proposed by different authors. In Wavelet Methods Visu Shrink, Neigh shrink and Bayes Shrink are implemented. The basic idea of wavelet methods is to denoise the image by applying wavelet transform to the noisy image, then thresholding the detailed wavelet coefficient and inverse transforming the set of thresholded coefficient to obtain the denoised image. The comparison of all filters methods is done using various Quality Metrics like Peak Signal-to-Noise Ratio (PSNR), Bit Error Rate (BER), Mean Square Error, etc. The filters methods implemented in MATLAB 7.10.0.499(R2010a).

scholarly journals Noise masking method based on an effective ratio mask estimation in Gammatone channels

2018 ◽  
Vol 7 ◽  
Author(s):  
Feng Bao ◽  
Waleed H. Abdulla
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Wiener Filter ◽  
Power Spectra ◽  
Auditory Scene Analysis ◽  
Accurate Estimation ◽  
Noise Power ◽  
Noise Masking ◽  
Time Frequency ◽  
Mask Estimation

In computational auditory scene analysis, the accurate estimation of binary mask or ratio mask plays a key role in noise masking. An inaccurate estimation often leads to some artifacts and temporal discontinuity in the synthesized speech. To overcome this problem, we propose a new ratio mask estimation method in terms of Wiener filtering in each Gammatone channel. In the reconstruction of Wiener filter, we utilize the relationship of the speech and noise power spectra in each Gammatone channel to build the objective function for the convex optimization of speech power. To improve the accuracy of estimation, the estimated ratio mask is further modified based on its adjacent time–frequency units, and then smoothed by interpolating with the estimated binary masks. The objective tests including the signal-to-noise ratio improvement, spectral distortion and intelligibility, and subjective listening test demonstrate the superiority of the proposed method compared with the reference methods.

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