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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. | ISSN:2249-5789 Sasi Rekha Sanivarapu et al, International Journal of Computer Science & Communication Networks,Vol 2(3), 328-333 Low Complexity Expert Dependent Noise Filtration Algorithm Sasi Rekha.Sanivarapu1 , P.Padmaja Priyadarsini 2, Dr.M.VenuGopala Rao3 1 PG Student, Narasaraopeta Engg. College, Narasaraopet, Guntur Dt. A.P. India Email: sasisanivarapu@gmail.com, 2 Assistant professor, Narasaraopet Engineering College, Narasaraopet, Guntur Dt. A.P., India Email:indirapadmaja@gmail.com 3 Professor, KL University, Vaddeswaram, Guntur Dt. A.P., India Email:mvgr03@hotmail.com Abstract- 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. KEYWORDS: filtering, Rician noise, speckle noise, Detection and Estimation. I. INTRODUCTION The image denoising plays a significant role in modern applications in various fields, including medical imaging and preprocessing for computer vision. Medical imaging acquisition technologies and systems introduce noise and artifacts in the images that should be attenuated by denoising algorithms. The denoising process, however, should not destroy anatomical details relevant from a clinical point of view. So, it is very difficult to suggest a robust method for noise removal which works equally well for different modalities of medical images. Also