  We propose an adaptive approach for non local means (NLM) image filtering
termed as non local adaptive clipped means (NLACM), which reduces the effect of
outliers and improves the denoising quality as compared to traditional NLM.
Common method to neglect outliers from a data population is computation of mean
in a range defined by mean and standard deviation. In NLACM we perform the
median within the defined range based on statistical estimation of the
neighbourhood region of a pixel to be denoised. As parameters of the range are
independent of any additional input and is based on local intensity values,
hence the approach is adaptive. Experimental results for NLACM show better
estimation of true intensity from noisy neighbourhood observation as compared
to NLM at high noise levels. We have verified the technique for speckle noise
reduction and we have tested it on ultrasound (US) image of lumbar spine. These
ultrasound images act as guidance for injection therapy for treatment of lumbar
radiculopathy. We believe that the proposed approach for image denoising is
first of its kind and its efficiency can be well justified as it shows better
performance in image restoration.

Computer Vision and Pattern Recognition