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.

A Denoising Mixed Noise Method Based on Median Filter and Lifting Wavelet Technology

2013 ◽  
Vol 411-414 ◽  
pp. 1546-1551 ◽  
Author(s):  
Zhong Tao Qiao ◽  
Feng Qi Gao ◽  
Guang Long Wang ◽  
Liang Liang Chang
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Detection Algorithm ◽  
Noise Detection ◽  
Image Digitization ◽  
Mixed Noise ◽  
The Common ◽  
Lifting Wavelet ◽  
Impulse Noise Detection ◽  
Suppress Noise

In image digitization and transmission, images often suffer contamination inevitably. The noises in images often consist of Gaussian noise and impulse noise. The common denoising algorithms are capable of removing single one of them. In order to remove those two types of noise, a composite algorithm is proposed. Firstly, based on median filter, an impulse noise detection algorithm is used to filter impulse noise. Secondly, adaptive directional lifting wavelet (ADL) and normal lifting wavelet is combined to suppress noise from image signal and protect the texture edge from loss simultaneously. Meanwhile an improved half-soft threshold is used for normal lifting wavelet. At last, simulations show that this technology can suppress Gaussian and impulse noise in image 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 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.

Decision Based Median Filter algorithm using Resource Optimized FPGA to Extract Impulse Noise

2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Rutuja Nandkumar Kulkarni ◽  
Pradip C Bhaskar
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Mean Value ◽  
Linear Filter ◽  
Noise Detection ◽  
Object Boundary ◽  
Typical Application ◽  
Impulse Noise Removal ◽  
Noisy Pixel

Median filter is a non-linear filter used in image processing for impulse noise removal. It finds its typical application in the situations where edges are to be preserved for higher level operations like segmentation, object recognition etc. This paper presents an accurate and efficient noise detection and filtering algorithm for impulse noise removal. The algorithm includes two stages: noise detection followed by noise filtering. The proposed algorithm replaces the noisy pixel by using  median value when other pixel values, 0’s or 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. Similarly algorithm checks for five different conditions to preserve image details, object boundary in high level of noise densities. This median filter was designed, simulated and synthesized on the Xilinx family of FPGAs (XC3S500E of Spartan-3E). The VHDL was used to design the above 2-D median filter using ISE (Xilinx) tool & tested & compared for different grayscale images.

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