Reduksi Noise Salt And Pepper Pada Citra Digital Menggunakan Metode Contraharmonic Mean Filter

MEANS (Media Informasi Analisa dan Sistem) ◽

10.54367/means.v2i1.17 ◽

2017 ◽

pp. 16-18

Author(s):

Pandi Barita Nauli Simangunsong

Keyword(s):

Digital Images ◽

Filter Method ◽

Black Spots ◽

Contraharmonic Mean ◽

Mean Filter ◽

Salt And Pepper

Image is an object that is most often used for personal or public purposes, but the image is very susceptible to interference such as noise or noise where the image is exposed to noise or noise will experience black spots attached to the image. The existence of noise attached to the image needs to be reduced so that the image looks clearer using the contraharmonic mean filter method that can reduce noise in digital images. The image affected by noise after being reduced using the contraharmonic mean filter method, the image is much clearer after being reduced.

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Enhancing the Performance of One Dimensional Median Filter

Restaurant Business ◽

10.26643/rb.v118i7.7249 ◽

2019 ◽

Vol 118 (7) ◽

pp. 73-76

Author(s):

Sharanabasappa ◽

P Ravibabu

Keyword(s):

Impulse Noise ◽

Rank Order ◽

Median Filter ◽

Filter Method ◽

Salt And Pepper Noise ◽

Edge Preservation ◽

Word Level ◽

Look Ahead ◽

Filter Architecture ◽

Salt And Pepper

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

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Machine learning based conditional mean filter: a non-linear extension of the ensemble Kalman filter

10.5194/egusphere-egu21-9566 ◽

2021 ◽

Author(s):

Truong-Vinh Hoang ◽

Sebastian Krumscheid ◽

Raul Tempone

Keyword(s):

Machine Learning ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Machine Learning Techniques ◽

Filter Method ◽

Gaussian State ◽

Correct Prediction ◽

Conditional Mean ◽

Non Linear ◽

Mean Filter

<p>Filtering is an uncertainty quantification technique that refers to the inference of the states of dynamical systems from noisy observations. This work proposes a machine learning-based filtering method for tracking the high-dimensional non-Gaussian state-space models with non-linear dynamics and sparse observations. Our filter method is based on the conditional expectation mean filter and uses machine-learning techniques to approximate the conditional mean (CM). The contribution of this work is twofolds: (i) we demonstrate theoretically that the assimilated ensembles obtained using the ensemble conditional mean filter (EnCMF) provide a correct prediction of the posterior mean and have the optimal variance, and (ii) we implement the EnCMF using artificial neural networks, which has a significant advantage in representing non-linear functions that map between high-dimensionality domains, such as the CM. We implement the machine learning-based EnCMF for tracking the states of the Lorenz-63 and 96 systems under the chaotic regime. Numerical results show that the EnCMF outperforms the ensemble Kalman filter.</p>

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Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37965 ◽

2021 ◽

Vol 9 (9) ◽

pp. 301-313

Author(s):

Vimal Chauhan

Keyword(s):

Image Processing ◽

Image Restoration ◽

Median Filter ◽

Processing Technique ◽

Image Processing Technique ◽

Digital Cameras ◽

Salt And Pepper Noise ◽

Satellite Imaging ◽

Mean Filter ◽

Salt And Pepper

Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

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