Improved Iterative Truncated Arithmetic Mean Filter

This thesis discusses image processing and filtering techniques with emphasis on Mean filter, Median filter, and different versions of the Iterative Truncated Arithmetic Mean (ITM) filter. Specifically, we review in detail the ITM algorithms (ITM1 and ITM2) proposed by Xudong Jiang. Although filtering is capable of reducing noise in an image, it usually also results in smoothening or some other form of distortion of image edges and file details. Therefore, maintaining a proper trade off between noise reduction and edge/detail distortion is key. In this thesis, an improvement over Xudong Jiang’s ITM filters, namely ITM3, has been proposed and tested for different types of noise and for different images. Each of the two original ITM filters performs better than the other under different conditions. Experimental results demonstrate that the proposed filter, ITM3, provides a better trade off than ITM1 and ITM2 in terms of attenuating different types of noise and preserving fine image details and edges

Further properties and a fast realization of the iterative truncated arithmetic mean filter

The iterative truncated arithmetic mean (ITM) filter has been recently proposed. It possesses merits of both the mean and median filters. In this brief, the Cramer-Rao lower bound is employed to further analyze the ITM filter. It shows that this filter outperforms the median filter in attenuating not only the short-tailed Gaussian noise but also the long-tailed Laplacian noise. A fast realization of the ITM filter is proposed. Its computational complexity is studied. Experimental results demonstrate that the proposed algorithm is faster than the standard median filter