A Blur Reducing Adaptive Filter for the Removal of Mixed Noise in Images

In this paper, an algorithm has been developed to remove additive mixed noise in images with edge preservation. The noise characteristics may vary in the same application from one image to another. In these environments, nonlinear general filters will not perform well and adaptive non-linear filters are best suited. The algorithm based on local statistics such as signal variance and noise Variance is considered. It depends on minimum mean square estimation of the corrupted signal. The signal variance and noise variance are calculated by moving signal window and moving noise window respectively. This offers optimal adaptive filtering in the homogeneous regions as well as in the edges. The adaptive alpha trimmed mean filters with a threshold value have been proposed to preserve edges. The performance of the filter in the presence of different types of noise are evaluated and compared with general mean, median and alpha trimmed mean filters. The image enhancement factor has been calculated as the performance-measuring factor. The Lena image has been considered to carryout the subjective and objective analysis of the proposed filter.