PERFORMANCE ANALYSIS OF LINEAR AND NON LINEAR FILTERS FOR IMAGE DE NOISING
Keywords:
De noising, average, median, Gaussian Alpha trimmed mean filter, salt and pepper, speckle and Poisson noise, PSNR, MSEAbstract
An image is corrupted by different types of noises. Noise is any desired information that
contaminates an image. Due to the presence of noise the information associated with the
image can be damaged. Image de noising is the process to remove the noise while retaining
as much as possible the important signal features. De noising can be done through both linear
and nonlinear filtering techniques. In this paper the performance analysis of average filter,
median filter, Gaussian filter, Alpha trimmed mean filter, Fuzzy logic based Alpha trimmed
median filter is analyzed. The distinctive feature of the all the proposed filters is that it offers
well line, edge, detail and texture preservation performance while, at the same time,
effectively removing noise from the input images. Here the Gaussian noise, salt and pepper
noise, speckle noise, Poisson noise are added to the images and then linear and nonlinear
filtering techniques are applied to the noisy images to remove the noise. The performance of
the filters is compared using PSNR, MSE. The experimental results show the comparison and
the better filtering techniques for the purpose of noise removal.
