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 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 Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanqiu Zeng ◽  
Baocan Zhang ◽  
Wei Zhao ◽  
Shixiao Xiao ◽  
Guokai Zhang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Electronic Noise ◽  
Mr Images ◽  
Electronic Components ◽  
Mr Image ◽  
Residual Part ◽  
Soft Threshold

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

scholarly journals Echocardiography image denoising using fractal wavelet transform

2018 ◽  
Vol 5 ◽  
pp. 23-33
Author(s):  
Reena Manandhar ◽  
Sanjeeb Prashad Pandey
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Hard Thresholding ◽  
Echocardiography Image ◽  
Different Types

One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.

scholarly journals Perbaikan Citra Digital dengan Menggunakan Filtering Technique dan Similarity Measurement

d'CARTESIAN ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Gybert Saselah ◽  
Winsy Weku ◽  
Luther Latumakulita
Keyword(s):  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Wiener Filter ◽  
Reduction Process ◽  
Input Image ◽  
Similarity Measurement ◽  
Gaussian Filter ◽  
Original Image ◽  
Noise Filter

Abstract Often the digital image can be contaminated with noise,  which usually occurs in the process of retrieval or storage of digital images and delivery process either via satellite or  cable . By using the technique of filtering noise reduction process will be performed on a digital image that has previously been given Gaussian noise and followed by a Similarity Measurement to identify similarities between  image filtered and original image. This study was conducted to determine the appropriate filtering techniques to reduce the Gaussian noise. Image processing in this study composed by the input image and read the image matrix, converting images, adding noise, denoising digital images by applying filters performed using Matlab R2012a software ( version 7.14.0.739) . Application of Gaussian filter with a value of = 1.0 produce a digital image that is closest to the original image than the application of a Gaussian filter with another value, for  . As for the application of the Wiener filter is seen that the greater the value, the resulting digital image will be closer to the original image. For further research can be done on other types of noise or to a combination of two or more noise. Keywords : Digital Image , Noise , Filter , Similarity Measurement. Abstrak Seringkali citra digital dapat terkontaminasi derau (noise), yang biasanya terjadi pada proses pengambilan ataupun penyimpanan citra digital serta proses pengiriman citra digital baik melalui satelit maupun melalui kabel juga. Dengan menggunakan teknik filtering akan dilakukan proses pengurangan noise pada suatu citra digital yang sebelumnya telah diberi Gaussian noise dan dilanjutkan dengan Similarity Measurement untuk mengidentifikasi kesamaan citra digital hasil filtering dengan citra original. Penelitian ini dilakukan untuk menentukan teknik filtering yang tepat untuk mengurangi Gaussian noise. Proses pengolahan citra dalam penelitian ini terdiri dengan proses input gambar dan membaca matriks citra, konversi citra, menambahkan noise, denoising citra digital dengan menerapkan filter yang dilakukan dengan menggunakan software Matlab R2012a (versi 7.14.0.739). Penerapan Gaussian filter dengan nilai = 1,0 menghasilkan citra digital yang paling mendekati citra original dibandingkan dengan penerapan Gaussian filter dengan nilai  lain, dimana . Sedangkan untuk penerapan Wiener filter terlihat bahwa semakin besar nilai , maka citra digital yang dihasilkan akan semakin mendekati citra original. Untuk penelitian selanjutnya dapat dilakukan pada jenis noise lain ataupun untuk gabungan dua noise atau lebih. Kata kunci: Citra digital, Noise, Filter, Similarity Measurement

scholarly journals Optimized Hybrid Model for Gaussian Noise Reduction images

2021 ◽  
Vol 19 (1) ◽  
pp. 9-17
Author(s):  
Lubna Farhi ◽  
Agha Yasir ◽  
Farhan Ur Rehman ◽  
Baqar A. Zardari ◽  
Ramsha Shakeel
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.

scholarly journals Comparative Image Quality Analysis of Spatial Filters for Pre-processing of CT Abdominal Images

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1449-1469
Author(s):  
Ramya Mohan ◽  
S.P. Chokkalingam ◽  
Kirupa Ganapathy ◽  
A. Rama
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Ct Images ◽  
Spatial Filters ◽  
Abdominal Ct ◽  
Wiener Filters ◽  
Salt And Pepper

Aim: To determine the efficient noise reduction filter for abdominal CT images. Background: Image enrichment is the first and foremost step that has to be done in all image processing applications. It is used to enhance the quality of digital images. Digital images are liable to addition of noise from various sources such as error in instrument calibration, excess staining of images, etc., Image de-noising is an enhancement technique used to remove / reduce noise present in an image. Reducing the noise of images and preserving its edges are always critical and challenging in image processing. Materials and Method: In this paper, four different spatial filters namely Mean, Median, Gaussian and Wiener were used on 100 CT abdominal images and their performances were compared against the following four parameters: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), Normalised correlation coefficient (NCC) and Normalised Absolute Error (NAE) to determine the best denoising filter for the abdominal CT images. Result: Based on the experimental parameters, the median filter had the maximum efficiency in managing salt and pepper noise than the other three filters. Both Median and Wiener filters showed efficiency in removing the Gaussian noise. Whereas, the Wiener filter demonstrated higher efficiency in reducing both Poisson and Speckle noise. Conclusion: Based on the results of this study, we can conclude that the median filter can be used to reduce Salt and Pepper noises. Median and Wiener filters are significantly better for Gaussian Noise and the Wiener filter can be used to reduce both Poisson & Speckle noise in abdominal CT images.

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 Hybrid FWF Model For Gaussian Noise Reduction From Images

10.31645/11 ◽  
2020 ◽  
Author(s):  
Lubna Farhi ◽  
◽  
Farhan Ur Rehman ◽  
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, the image efficiency is improved by using hybrid model of wiener’s filter and fuzzy filter. It’s a challenging task to remove Gaussian noise (GN) from an image and to protect the picture edges. The Fuzzy - Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results presented that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques

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

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