• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.573-578
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• 2014

In this letter, we propose a noise reduction method for use in magnetic resonance images that is based on non-local mean and guided image filters. Our method consists of two phases. In the first phase, the guidance image is obtained from a noisy image by using an adaptive non-local mean filter. The spread of the kernel is adaptively by controlled by implementing the concept of edgeness. In the second phase, the noisy images and the guidance images are provided to the guided image filter as input in order to produce a noise-free image. The improved performance of the proposed method is investigated by conducting experiments on standard datasets that contain magnetic resonance images. The results show that the proposed scheme is superior over the existing approaches.