Adaptive Window Length Recursive Weighted Median Filter for Removing Impulse Noise in Images with details Preservation
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Abstract
An adaptive window length Recursive Weighted Median filter [ARWMF] for removing the impulse noise with better edge and ¯ne detail preservation is presented. Larger window size may blur the images and the lower window size does not remove the noise at high density. To overcome this, the window size of the RWMF is adaptive based on the presence of noise density. Median controlled algorithm is used to calculate the weights for the RWMF. In median controlled algorithm, the ¯lter gives the smallest weight for the impulse. However, for many weight functions, including the exponential one, this weight is non-zero. Thus the impulse has an effect on the output and the magnitude of the impulse is reduced. The computational complexity for the weight calculation is simple and it is very efficient. The window size of the RWMF is adaptive based on the presence of noise density. The proposed algorithm produces better edge and fine details preservations and reduces blurring at the high density impulse noise.The performance of the proposed algorithm is given in terms of mean square error (MSE), mean absolute error (MAE) and peak signal to noise ratio (PSNR) and it is compared with Standard Median ¯lters, Weighted Median filters, Center Weighted Median ¯lters, Recursive Weighted Median filters and Lins Adaptive length Recursive weighted median filters using Median Controlled Algorithm.
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