Synthetic Aperture Radar ( SAR) images are contaminated by multiplicative noise, due to the coherence of the radar wavelength, labeled as speckle noise which results in an important reduction in the efficiency of target detection and classification algorithms . In this paper the corrupted pixels are replaced by an estimated value using the simple filter based statistics filters with nonlinear function which are worked at the same time to reduce the speckle noise without blurring edges or other features in SAR imagery. Quantitative and qualitative comparisons of the results obtained by the proposed method with the results achieved from the other speckle noise reduction filters demonstrate its higher performance for speckle reduction with preserving high frequency features (edges) in SAR images.
Iris recognition is regarded as the most reliable and accurate biometric identification system available highly protected and stable. Iris situating is the main focus in the procedure of iris recognition and verifies the precision of identification. In this work, a new algorithm for iris localization is suggested based on the median filter and the histogram to determine an automated global threshold and the pupil centre. An algebraic based on semi-discrete matrix decomposition SDD is used to extract iris feature from iris image that decrease the difficulty in input layered neural network, the sizes of input patterns are enhanced. The iris recognition is developed by a neural network with differential adaptive learning rate to identify the iris features. This method is simple, effective and high speed recognition. The system is implemented by using Matlab. Experimental outcomes indicate that the suggested algorithm gives the accuracy of 100 % with time equal 1.4 sec is best than other methods for Daugman and Wildes.
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