scholarly journals A Noise Adaptive Approach to Impulse Noise Detection and Reduction

2015 ◽  
Vol 15 (1) ◽  
pp. 67-76 ◽  
Author(s):  
Isma Irum ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Mudassar Raza ◽  
Faisal Azam
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Noise Detection ◽  
False Detection ◽  
Second Stage ◽  
Objective Quality ◽  
Noise Density ◽  
Filter Approach ◽  
Two Stages ◽  
Impulse Noise Detection

A noise adaptive filter has been proposed in this study aiming to estimate the original image pixel values in the presence of impulse noise in monochromatic images. The proposed filter approach is noise adaptive that as the percentage of noise density increases in the image, the size of neighborhood in filtering window is also increased. Proposed approach comprises of two stages, one is impulse noise detection and the other is impulse noise reduction or cancellation. First stage is based on median and mean distance and thresholding whereas the second stage is based on reconstruction of the image using the values of neighboring pixels of the pixel under consideration detected as contaminated pixel by first stage. Reconstruction is done by estimating reference values using uncorrupted pixels in the neighborhood of pixel under consideration. The proposed method has been compared to various existing methods by using peak signal to noise ratio (PSNR) for measuring the objective quality strength. To measure the impulse noise detection the method has also been compared with other existing methods using the ratio of mis  etection (MD) and false detection (FD).DOI: http://dx.doi.org/10.3126/njst.v15i1.12016  Nepal Journal of Science and TechnologyVol. 15, No.1 (2014) 67-76

scholarly journals The statistical analysis of random-valued impulse noise detection techniques based on the local image characteristic: ROAD, ROLD and RORD

2019 ◽  
Vol 15 (2) ◽  
pp. 794
Author(s):  
Vorapoj Patanavijit ◽  
Kornkamol Thakulsukanant
Keyword(s):  
Statistical Analysis ◽  
Statistical Characteristic ◽  
Impulse Noise ◽  
Detection Accuracy ◽  
Noise Detection ◽  
Detection Techniques ◽  
Noise Density ◽  
Relative Differences ◽  
Impulse Noise Detection ◽  
Local Image

<p>Advances in local image statistical analysis have made possible the random-valued impulse noise detection but the current noise detections based on ROAD (Rank-Ordered Absolute Differences), ROLD (Rank-Ordered Logarithmic Differences) and RORD (Rank-Ordered Relative Differences), which are the most three effective and practical detections using the local image statistical characteristic, operates effectively on different noise density and different image statistical characteristic. To address these issues, this paper proposes the comparative analysis on the noise detections based on ROAD, ROLD and RORD. Therefore, the first contribution is the comparative statistical distribution of these three noise detections. By comprehensive experiment at each noise density, the optimized detected threshold is later determined from four benchmark data: Lena, Girl, Pepper and Airplane. Moreover, the maximum detection accuracy for each case is comparatively demonstrated by using the noise detections based on ROAD, ROLD and RORD with the optimized detected threshold.</p>

scholarly journals An Efficient Method for a Specific Case of Detecting Impulse Noise on Scanned Documents

2021 ◽  
Author(s):  
Petar Prvulović ◽  
Jelena Vasiljević ◽  
Dhinaharan Nagamalai
Keyword(s):  
Large Scale ◽  
Impulse Noise ◽  
Detection Method ◽  
Detection Accuracy ◽  
Primary Concern ◽  
Noise Detection ◽  
Second Stage ◽  
Margin Detection ◽  
Two Stages ◽  
Scanned Documents

This paper explains a method used to detect the presence of impulse noise in a set of scanned documents as a part of OCR preprocessing. As the document set is supposed to be processed in large scale, the primary concern of the noise detection method was efficiency within existing project constraints. Following the nature of noise, the method seeks to detect the presence of noise in document margins. The method works in two stages. First stage is margin detection, based on color spectre analysis. Second stage is noise recognition in margin samples, based on a pixel contrast score. The resulting implementation proved efficient both in terms of detection accuracy and algorithmic complexity.

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．

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