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Low Complexity Expert Dependent Noise Filtration Algorithm

In this paper, a flexible and robust wavelet based image denoising algorithm is proposed, which adapts itself to various and unknown types of noise as well as to the preference of the medical expert: a single tuning parameter is used to balance the preservation of relevant details against the degree of noise reduction. We employ a preliminary coefficient classification technique to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The proposed algorithm is of low-complexity, both in its implementation and execution time. The results show that its usefulness for denoising and enhancement of the CT, Ultrasound and Magnetic Resonance images.