An Variable Coefficient Images Denoising Method

2013 ◽  
Vol 333-335 ◽  
pp. 832-835
Author(s):  
Da Peng Zhou
Keyword(s):  
Gaussian Noise ◽  
Variable Coefficient ◽  
Impulse Noise ◽  
Denoising Method ◽  
Noise Image ◽  
Black Noise ◽  
Gray Values ◽  
Average Filter ◽  
Coefficient Method

Proposed An images denoising method by using variable coefficient in filtering. The key point is using different coefficient to minimize the influence of each gray values of the pixels as is regarded as black noise and white noise and choosing a threshold to decided which filter will be used as the most suitable one. Usually,a image is polluted by Gaussian noise and impulse noise simultaneously, It cannot be obtained if only using one of them,though both median and average filter are used inimage denoising in obtain the edge as well as detail characteristics of impulse noise image and smoothing Gaussiannoise respectively when denoising in spatial space. Experimental results show that the variable coefficient method can improve the quality of images than the classical methods.

Mixed Noise Image De-Noising Based on EM Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4734-4741 ◽  
Author(s):  
Gui Cun Shi ◽  
Fei Xing Wang
Keyword(s):  
Applied Sciences ◽  
Wavelet Analysis ◽  
Em Algorithm ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Noise Source ◽  
High Quality ◽  
Noise Image ◽  
Mixed Noise ◽  
Non Gaussian

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

A Mixed Denoising Method Based on Median Filter and Lifting Wavelet Technology for Sewage Sensing Signal Treatment

2013 ◽  
Vol 330 ◽  
pp. 967-972 ◽  
Author(s):  
Ai Ai Fan ◽  
Guang Long Wang
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Signal Acquisition ◽  
Denoising Method ◽  
Lifting Wavelet ◽  
Aeration Volume ◽  
Signal Treatment

Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter is given to filter impulse signal, also a lifting wavelet was constructed by lifting original wavelet. The threshold based on lifting wavelet transform for signal was applied to denoising gaussian noise. Simulations are conducted on the presented algorithm, and the simulation result shows that this algorithm can remove mixed Gaussian and impulse noise in signal efficiently.

Infrared Image Denoising Based on Peer Groups with Median Filtering and Wiener Filtering

2013 ◽  
Vol 401-403 ◽  
pp. 1059-1062 ◽  
Author(s):  
Bao Shu Li ◽  
Ke Bin Cui ◽  
Xue Tao Xu ◽  
Wen Li Wei
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Peer Group ◽  
Median Filter ◽  
Infrared Image ◽  
Wiener Filtering ◽  
Median Filtering ◽  
Denoising Method ◽  
Sobel Operator ◽  
Boundary Information

With characteristics of impulse noise and Gaussian noise, we propose a new denoising method to infrared image. We use Sobel operator to obtain boundary information, and determine the denoising method based on the pixel number of the peer group, denoising impulse noise and Gaussian noise with median filter and Wiener filtering. Experimental results are provided to show that the proposed filter achieves a promising performance in PSNR and boundary information, compared with the median filtering, Wiener filtering and peer group algorithms.

scholarly journals High Density Noise Reduction of Tea Leaves using Density Mass Filter (DMF)

2019 ◽  
Vol 8 (11) ◽  
pp. 2988-2991
Keyword(s):  
Gaussian Noise ◽  
Classification Accuracy ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Salt And Pepper Noise ◽  
Tea Leaves ◽  
Signal To Noise ◽  
Mass Filter ◽  
Salt And Pepper

In agriculture digital image processing play an important role in the prediction of tea leaves diseases. But acquisition of image may be corrupted by various types of noise such as impulse noise, Gaussian noise and salt and pepper noise. These noises can corrupt the image. So it will reduce the quality of the image and it reduces the classification accuracy. Hence it needs a efficient filter to remove these noise. This paper introduced a new filter density mass filter. It reduces all kinds of noise. Two metrics PSNR (Peak Signal to Noise ratio) and RMSE (Root Mean Square Error) values are used to evaluate the quality of images. The PSNR value of proposed filter is significantly high and RMSE value is reasonably low

scholarly journals A Functioning Model of a Dual-Band Radar System That Classifies Targets under the Influence of Interference and Noise

2020 ◽  
Vol 6 (4) ◽  
pp. 112-119
Author(s):  
V. Makarenkov
Keyword(s):  
Gaussian Noise ◽  
Radar System ◽  
White Gaussian Noise ◽  
Dual Band ◽  
Correct Classification ◽  
Noise Interference ◽  
Active Noise ◽  
Complex Target

There is proposed a model of a signal received from a complex target formed by a set of rapidly fluctuating point reflectors. Signal reception is carried out against the background of narrow-broadband active noise interference and white Gaussian noise. A functioning model of a dual-band radar system is proposed, in which the problem of classifying rapidly fluctuating point reflectors as a part of complex target against the background of interference and noise is solved. The article examines the issue of assessing the quality of this model, as well as meeting the re-quirements for ensuring a given value of the probability of correct classification of goals.

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

Sign in / Sign up

Export Citation Format

Share Document