Impulse Noise Suppression Method by Median Filtering with Parity Extraction

2013 ◽  
Vol 846-847 ◽  
pp. 991-994
Author(s):  
Zhen Xing Li
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Median Filter ◽  
Window Size ◽  
New Method ◽  
Median Filtering ◽  
Experiment Data ◽  
Simulation And Experiment ◽  
Suppression Method

A new impulse noise suppression method by median filtering with parity extraction was proposed in this paper. The window size of the median filter has important effect on the performance of the filtering result, larger window size can suppress impulse noise effectively but often at cost of loss of the detail information of the signal, while smaller window size can protect the detail information better but results in degrading of the noise suppression. Parity extraction is done to the signal at first and median filtering carries on the odd and even part respectively, and then a new method of median filtering with short window size to suppress the impulse noise is obtained. Simulation and experiment data of telemetry process results show the effectiveness of the proposed method.

Impulse Noise Suppression Method for Telemetry Signal by Multi-Scale Median Filtering

2014 ◽  
Vol 989-994 ◽  
pp. 3726-3729
Author(s):  
Xiu Fang Liu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Window Size ◽  
Frequency Domain Analysis ◽  
Median Filtering ◽  
Multi Scale ◽  
Filtering Technique ◽  
Suppression Method

The telemetry signal is often interfered with impulse noise, which results in difficulty in time domain and frequency domain analysis results. Hereby a new impulse noise suppression method based on wavelet transform and median filtering technique was proposed. The received signal is decomposed into detailed components and approximate components, and then the median filtering is carried on the wavelet decomposition components with vary filtering window size according to the wavelet transform scale respectively. This method can suppress the impulse noise effectively and keep the detail information of the signal from the loss at the same time. The simulation and experimental results prove the effectiveness of the method.

scholarly journals Execution of Median Filter Built on FPGA for Trimming Noise Meeting the Real Time Requirements

2019 ◽  
Vol 8 (4) ◽  
pp. 7855-7858
Keyword(s):  
Real Time ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Median Filter ◽  
Sliding Window ◽  
Spatial Filter ◽  
Effective Technique ◽  
The Real ◽  
Time Requirements ◽  
Suppression Process

As images plays a vital in all aspects, there is a need to met the real time requirements in processing the image. Major challenges raised in processing the image is noise. The utmost typical difficult is effective denoising creation as well as quick functioning in the processing of digital image noise suppression process for the need of real time consequences to afford image with high quality this project was introduced. Generally filters plays a major role to remove the impulse noise in acquired images. The filter named sliding window spatial filter which is familiar as median filter is effective technique to eradicate impulse noise from the devoleped image. But in real time, it is very difficult to execute. To overcome this, FPGA methodology is introduced to fulfills the support besides the optimization of major constraints like area, speed, power. In addition to this, it assures technical sustenance of eradicating noise in image as per requirements in real time. Regarding the design and structure appearances in FPGA, Xilinx software is used for simulation and code has been written in Verilog language.

scholarly journals A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

2020 ◽  
Vol 9 (4) ◽  
pp. 1305-1309
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Domain ◽  
Median Filtering ◽  
Impulse Noise Removal ◽  
Noise Density ◽  
Improved Accuracy ◽  
Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

Insulator Image Denoising Based on the Simplified PCNN Model

2013 ◽  
Vol 718-720 ◽  
pp. 2092-2098 ◽  
Author(s):  
Dan Li ◽  
Hong Ying He ◽  
Yi Jia Cao ◽  
Dian Sheng Luo
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Infrared Image ◽  
Window Size ◽  
Denoising Method ◽  
Similarity Calculation ◽  
Image Edge ◽  
Filter Noise ◽  
Better Than

A new denoising method was proposed in the paper according to the characteristics of insulator infrared image with impulse noise. First, based on the pulse coupled neural network (PCNN) to detect the location of the impulse noise pixels, while maintaining the same non-noise pixels. and then according to the characteristics of the impulse noise, the window size of the filter was adaptively determined by calculating the noise intensity of the image. The pixels with maximum and minimum gray value in filtering window are excluded, using the left pixels similarity calculation out weights. A new weighted filtering algorithm is used to filter noise pixels. The experiments show that the method is better than the median filter in peak signal-to-noise ratio (PSNR), and has better image edge details protection ability.

Application of Improved Median Filtering Algorithm to Image De-Noising

2014 ◽  
Vol 998-999 ◽  
pp. 838-841
Author(s):  
Wen Long Jiang ◽  
Guang Lin Li ◽  
Wei Bing Luo
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Real Time Processing ◽  
Time Processing ◽  
Filtering Algorithm ◽  
Weighted Median ◽  
The Mean ◽  
Weighted Median Filter ◽  
Mean Filtering

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

FPGA-Based Re-Configurable Architecture for Window-Based Image Processing

2018 ◽  
pp. 273-310
Author(s):  
Kamarujjaman Sk ◽  
Manali Mukherjee ◽  
Mausumi Maitra
Keyword(s):  
Adaptive Filtering ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Digital Images ◽  
Median Filter ◽  
Hardware Architecture ◽  
Efficient Design ◽  
Filtering Method ◽  
Switching Median Filter ◽  
Noise Density

In this proposed book chapter, a simple but efficient presentation of Median Filter, Switching Median Filter, Adaptive Median Filter and Decision-Based Adaptive Filtering Method and their hardware architecture for FPGA is described for removal of up to 99% impulse noise from Digital Images. For hardware architecture, simulation is done using Xilinx ISE 14.5 software of XILINX. For implementation, these approaches utilize Genesys VIRTEX V FPGA device of XC5VLX50T device family. In this approach, we proposed an efficient design for suppression of impulse noise from digital images corrupted by up to 99% impulse noise using decision based adaptive filtering method as well as preserve the details of image. The method works in two different stages – noise detection using switching technique and finally noise suppression and restoration. Experimental results show that our method perform better in terms of PSNR below 80% noise density but above 80% noise density it is almost comparable with the latest methods.

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