De-noising magnetic resonance images (MRI) has recently become an interesting topic in medical diagnosis applications. Many algorithms have been proposed for this purpose. However, these algorithms usually suffer from...
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ISBN:
(纸本)9781479945801
De-noising magnetic resonance images (MRI) has recently become an interesting topic in medical diagnosis applications. Many algorithms have been proposed for this purpose. However, these algorithms usually suffer from poor performance or time consumption. In this paper, we propose a 2-D version of the recently proposed convex recursiveinverse (RI) algorithm that provides fast convergence at the beginning to save time and then provides high performance in terms of noise removal. To test the algorithm, a de-noising experiment has been conducted on MR image that is assumed to be corrupted by an additive white Gaussian noise (AWGN). Simulations show that the proposed algorithm successfully recovers the image.
The recently proposed recursiveinverse (RI) algorithm was shown to have a similar mean-square-error (mse) performance as the recursive-Least-Squares (RLS) algorithm with reduced complexity. The selection of the forge...
详细信息
ISBN:
(纸本)9781457702013
The recently proposed recursiveinverse (RI) algorithm was shown to have a similar mean-square-error (mse) performance as the recursive-Least-Squares (RLS) algorithm with reduced complexity. The selection of the forgetting factor has a significant influence on the performance of the RLS algorithm. The value of the forgetting factor leads to a tradeoff between the stability and the tracking ability. In a system identification setting, both the filter length and a leakage phenomenon affect the selection of the forgetting factor. In this paper, we first analytically show that this leakage phenomenon and the filter length have much less influence on the performance of the RI algorithm. Simulation results, in a system identification setting, validate the theoretical results.
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