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Statistical interpretation of non-local means

Statistical interpretation of non-local means

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© The Institution of Engineering and Technology
Published

Noise filtering is a common step in image processing, and is particularly effective in improving the subjective quality of images. A large number of techniques have been developed, many of which concentrate on the problem of removing noise without damaging small structures such as edges. One recent approach that demonstrates empirical merit is the non-local means (NLM) algorithm. However, in order to use noise filtering algorithms in quantitative or clinical image analysis tasks an understanding of their behaviour that goes beyond subjective appearance must be developed. The purpose of this study is to investigate the statistical basis of NLM in order to attempt to understand the conditions required for its use. The theory is illustrated on synthetic data and clinical magnetic resonance images of the brain.

Inspec keywords: filtering theory; biomedical MRI; statistical analysis; brain; image denoising

Other keywords: noise filtering algorithms; clinical image analysis tasks; clinical magnetic resonance images; statistical interpretation; nonlocal means; image processing

Subjects: Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Biology and medical computing; Optical, image and video signal processing; Biomedical magnetic resonance imaging and spectroscopy; Filtering methods in signal processing; Other topics in statistics; Probability theory, stochastic processes, and statistics; Other topics in statistics; Medical magnetic resonance imaging and spectroscopy

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