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

Comparison between Mean Filter and Median Filter Algorithm in Image Denoising Field

2014 ◽  
Vol 644-650 ◽  
pp. 4112-4116 ◽  
Author(s):  
Xiao Xin Sun ◽  
Wei Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Signal To Noise ◽  
Denoising Method ◽  
Mean Filter ◽  
Noise Ratio

An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.

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.

Preliminary study of improved median filter using adaptively mask size in light microscopic image

Microscopy ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 31-36
Author(s):  
Ji-Youn Kim ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Coefficient Of Variation ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Microscopic Image ◽  
Signal To Noise ◽  
Mask Size ◽  
Preliminary Study ◽  
Noise Ratio ◽  
Image Details

Abstract This study aimed to develop and evaluate an improved median filter (IMF) with an adaptive mask size for light microscope (LM) images. We acquired images of the mouse first molar using a LM at 100× magnification. The images obtained using our proposed IMF were compared with those from a conventional median filter. Several parameters such as the contrast-to-noise ratio, coefficient of variation, no-reference assessments and peak signal-to-noise ratio were employed to evaluate the image quality quantitatively. The results demonstrated that the proposed IMF could effectively de-noise the LM images and preserve the image details, achieving a better performance than the conventional median filter.

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.

Comparative Study on Filter Methods of Medical Ultrasound Images

2011 ◽  
Vol 341-342 ◽  
pp. 467-471
Author(s):  
Run Xia Ma ◽  
Xu Ming Zhang ◽  
Ming Yue Ding ◽  
Qi Liu
Keyword(s):  
Comparative Study ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Minimum Mean Square Error ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Protection Factor ◽  
Noise Ratio

This paper presents a comparative study on six despeckling methods such as modified hybrid median filter, gabor filter, speckle reducing anisotropic diffusion, homomorphic filter, non-local mean filter and squeeze box filter. We select eight objective evaluation parameters, such as signal-to-ratio, contrast signal–to–noise ratio, figure of merit, least absolute error, peak signal-to-noise ratio, edge protection factor, quantitative parameters of despeckling, signal-to-minimum mean square error ratio, to quantify the performance of these filters. The comparative study will provide a good guidance for selecting a suitable filter in the ultrasound image processing.

scholarly journals DEVELOPMENT OF A SYSTEM FOR DIGITAL WATERMARKS EMBEDDING INTO IMAGES BASED ON DCT-LWT-SVD

2020 ◽  
pp. 23-31
Author(s):  
A.V. Akhmametieva ◽  
A.A. Baraniuk
Keyword(s):  
Wavelet Transform ◽  
Copyright Protection ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Low Frequency ◽  
Digital Watermark ◽  
Signal To Noise ◽  
Actual Problem ◽  
Noise Ratio ◽  
Salt And Pepper

Copyright protection of digital content is a rather actual problem of humanity in the 21st century. Misuses of multimedia content is very common, and their number is growing with each passing day. One type of copyright protection is the embedding of digital watermark (DW) in the content. In this paper a new method of embedding digital watermark into image using discrete cosine transform, lifting wavelet transform (LWT) with maternal wavelet "Dobeshi-8" and singular coefficients decomposition is proposed. Embedding is performed into the first singular number of the low frequency wavelet transform region. As a digital watermark, we will use a grayscale image normalized to a range from zero to ten to provide a high peak signal-to-noise ratio (PSNR). The research analyzed the developed method: the method of embedding and detecting information was tested for its resistance to various types of attacks, namely: application of noise overlay (Gauss and pulse noise, "salt and pepper"), "unsharp" filter and median filter, and compression attack (with quality coefficients for a complete container from 60 to 100). As a result of the conducted testing, it was established that the method is quite resistant to all the attacks, except for the "unsharp" filtering (the resulting performance is not satisfactory). The method showed good results in peak signal-to-noise ratio - the average PSNR value is 50.5 dB, as well as high rates of similarity between the embedded DW and the extracted one - from 77% to 97.6% while saving the full container in a lossless format, and up to 53, 05 dB and 91.96% while saving the image in a lossless format (JPEG).

scholarly journals PERBANDINGAN METODE UNTUK PERBAIKAN KUALITAS CITRA MAMMOGRAM

2016 ◽  
Vol 7 (2) ◽  
pp. 657
Author(s):  
Hanifah Rahmi Fajrin
Keyword(s):  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Region Of Interest ◽  
Contourlet Transform ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Kanker payudara merupakan pembunuh nomor satu pada wanita di seluruh dunia. Sejauh ini, deteksi dari kanker payudara hanya menggunakan mata telanjang dan berdasarkan jam terbang (pengalaman) dari dokter dan radiologis. Terdapat beberapa kelemahan dalam menganalisis mammogram payudara guna mendeteksi keberadaan kanker payudara. Hal ini bisa diakibatkan oleh sel kanker yang tertutup oleh noise, kontras citra yang rendah dan faktor manusiawi lainnya seperti : kelelahan, mood, dan lainnya. Untuk meminimalisir hal tersebut dibutuhkan suatu metode yang dapat membantu dokter dalam menganalisis citra mammogram payudara. Pada penelitian ini, dilakukan suatu proses yang bertujuan untuk meningkatkan kualitas mammogram agar lebih memudahkan dokter dalam mendiagnosis kelainan pada payudara. Citra yang diolah merupakan citra mammogram yang tidak dipangkas dan tidak disegmentasi pada bagian Region of Interest (ROI), melainkan keseluruhan citra payudara setelah dihilangkan background yang berlebihan. Tahapan pada proses peningkatan kuliatas citra mammogram ini (pra pengolahan) terdiri dari : menghilangkan label atau artifak yang ditemukan pada mammogram, meng-crop citra pada bagian payudara saja (menghilangkan background), memperbaiki kontras citra dengan membandingkan beberapa metode yaitu: CLAHE, Non Subsampled Contourlet Transform (NSCT), Contras Stretching (CS) dan selanjutnya dilakukan smoothing dengan menggunakan filter median. Kinerja dari setiap metode dihitung dengan mencari nilai Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR) citra. Dari nilai MSE dan PSNR yang didapatkan, ditemukan nilai MSE dan PSNR terbaik pada metode NSCT dengan nilai 50.20878 db 31.75332 db. Kata kunci: CLAHE, NSCT, CS, median filter.

scholarly journals Proposed De-noising Algorithm

2020 ◽  
Vol 6 (2) ◽  
pp. 90-96
Author(s):  
Nawafil Abdulwahab Ali ◽  
Imad Al Shaikhli
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Efficient Algorithms ◽  
Signal To Noise ◽  
Crucial Step ◽  
Weighted Median ◽  
Noise Ratio ◽  
Weighted Median Filter ◽  
Grayscale Images

minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).

scholarly journals Analisa Perbandingan Adaptif Median Filter Dan Median Filter Dalam Reduksi Noise Salt & Pepper

2016 ◽  
Vol 2 (2) ◽  
pp. 157 ◽  
Author(s):  
Ivan Maulana ◽  
Pulung Nurtantio Andono
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Filter Noise ◽  
Audio Video

Suatu data atau informasi disajikan tidak hanya berupa data teks tetapi juga dapat berupa audio, video, dan gambar. Pada zaman sekarang informasi sangatlah penting dan diperlukan, begitu juga informasi yang terdapat pada citra. Citra (image) atau istilah lain untuk gambar merupakan salah satu komponen multimedia yang berperan penting sebagai bentuk informasi visual. Dibandingkan dengan data teks, citra memiliki banyak informasi. Namun terkadang citra juga dapat mengalami penurunan yaitu degradasi atau penurunan kualitas yang disebabkan oleh derau / noise, warna terlalu kontras, kabur, dan lain-lain. Ada beberapa jenis noise dalam pengolahan citra salah satunya yaitu Salt & Pepper noise. Noise Salt & Pepper berbentuk seperti bintik hitam dan putih pada citra. Untuk mengurangi noise ini dibutuhkan suatu metode, salah satunya yaitu median filter. Metode yang digunakan pada penelitian ini adalah median filter dan adaptif median filter. Perbedaan mendasar antara kedua metode ini yaitu pada besarnya windows pada adaptif median filter adalah variabel. Dari hasil penelitian, citra yang menggunakan metode adaptif median filter lebih baik daripada median filter. Dari perhitungan Peak Signal to Noise Ratio (PSNR) citra yang menggunakan adaptif median filter mendapatkan 29,2495 dB sedangkan median filter mendapatkan 23,8181 dB.Kata Kunci: Median filter, Adaptif Median filter, Noise salt & pepper, PSNR

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