19-03-2012, 03:26 PM
A NEW FAST AND EFFICIENT ALGORITHM FOR REMOVAL OF HIGH-DENSITY IMPULSE NOISES
Abstract:
A new algorithm is proposed for restoration of images that are highly corrupted by impulse noise.
The new algorithm shows significantly better image quality than a standard median filter (SMF), adaptive median filters (AMF), a threshold decomposition filter (TDF), cascade, and recursive nonlinear filters.
The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighbouring pixel value.
As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level
Existing System :
Standard Median Filter.
Adaptive Median Filter.
Threshold Median Filter.
Drawbacks Of Existing System:
The main drawback of a standard median filter (SMF) is that it is effective only for low noise densities.
At high noise densities, SMFs often exhibit blurring for large window sizes and insufficient noise suppression for small window sizes [7], [8].
To overcome the above drawbacks have an adaptive median filter (AMF) to the noisy pixels to preserve the edges and noise suppression. The main drawback of this method is that the processing time is very high because it uses a very large window size
Proposed system:
The Proposed Algorithm processes the corrupted image by first detecting the impulse noise.
The detection of noisy and noise-free pixels is decided by checking whether the value of a processed pixel element lies between the dynamic range (0, 255).
If the value of the pixel processed is within the range, then it is an uncorrupted pixel and left unchanged.
If the value does not lie within this range, then it is a noisy pixel and is replaced by the median value of the window or by its neighbourhood values.
Steps Of Proposed System:
Step 1) A 2-D window “Sxy” of size 3*3 is selected. Assume the pixel to be processed is P(X, Y).
Step 2) The pixel values inside the window are sorted, and Pmin,Pmax, and Pmed are determined as follows.
a) The rows of the window are arranged in ascending order.
b) The columns of the window are arranged in ascending order.
c) The right diagonal of the window is now arranged in ascending order.
Now the first element of the window is the minimum value Pmin, the last element of the window is the maximum value Pmax, and the middle element of the window is the median value Pmed.