Restoration of noise corrupted images using new 2-D window families

The objective of this work is the introduction of new 2-D window families used for design of FIR lowpass filters, with their application in filtering the images degraded by noise. These variable parameter window families based on the special properties of hyperbolic functions create pairs of transforms with a similar shape. The designed filters are applied to process a real image corrupted by pseudo-random Gaussian and speckle noise, respectively. The results show that the proposed windows provide improved mean absolute error, mean square error and signal to noise ratio performance over other classic window functions. Results of median and Wiener filters are also included for comparison