High Density Salt and Pepper Noise Removal in color and grayscale images Through Modified DBUTMF
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 7
Abstract
In most of the image processing applications, the image denoising is one of the main topic. Corrupted image is called the noisy image, and the corrected is called the de-noised image. We have different types of noises in the image processing like Gaussian noise, speckle noise, random noise, Salt&pepper noise etc. Among these the Salt and pepper noise is very dangerous noise compare to other noises. By using different algorithms we can reduce the noise from image. Now a days the color image processing is very widely used in image processing applications. In this project A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0’s and 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. This proposed algorithm shows better results errors in transmission or introduced during the signal acquisitionstage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. Salt and pepper noise can corrupt the images where the corrupted pixel takes either maximum or minimum gray level. Several nonlinear filters have been proposed for restoration of images contaminated by salt and pepper noise. Among these standard median filter has been established as reliable method to remove the salt and pepper noise without damaging the edge details. Main drawback of standard Median Filter (MF) is that the filter is effective only at low noise densities [1].When the noise level is over 50% the edge details of the original image will not be preserved by standard median filter. Adaptive MedianFilter (AMF) [2] perform well at low noise densities. But at high noise densities the window size has to be increased which may lead to blurring the image. In switching median filter [3], [4] the decision is based on a pre-defined threshold value. The major drawback of this method is that defining a robust decision is difficult. Also these filters will not take into account the local features as a result of which details and edges may not be recovered satisfactorily, especially when the noise level is high. To overcome the above drawback, Decision Based Algorithm (DBA) is proposed [5]. In this, image is denoised by using a 3X3 window. If the processing pixel value is 0 or 255 it is processed or else it is left unchanged. At high noise density As an advanced method compared with standard median filtering, the Adaptive Median Filter performs spatial processing to preserve detail and smooth non-impulsive noise. The window is replaced by the mean or the median value.this filter swiches in between Mean and Median.A prime benefit to this adaptive approach to median filtering is that repeated applications of this Adaptive Median Filter do not erode away edges or other small structure in the image.this filter also not able to work at the high noise densities.To overcome this drawback the Decision based algorithem is proposed.
Authors and Affiliations
K. Prasad , B. Jayanth nadh , B. Hymavathi
Keywords
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