Abstract
Magnetic resonance images are affected by noise of various types, which provide a hindrance to accurate diagnosis. Thus, noise reduction is still an important and difficult task in case of MRI. The objective behind denoising of images is to effectively decrease the unwanted noise by retaining the image features. Many techniques have been proposed for denoising MR images, and each technique has its own advantages and drawbacks. Nonlocal means (NLM) is a popular denoising algorithm for MR images. But it cannot be applied in its original form to different applications. The goal of this paper is to present the various optimization techniques for NLM filtering approach to reduce the noise present in MRIs. The original NLM filters along with its various advancements and mathematical models have been included.
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Nikita Joshi, Sarika Jain (2016). Optimization of Nonlocal Means Filtering Technique for Denoising Magnetic Resonance Images: A Review. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_1
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