An Approach for Reducing Outliers of Non Local Means Image Denoising Filter

TL;DR

This paper introduces NLACM, an adaptive non-local means filter that reduces outliers and enhances denoising quality, especially at high noise levels, demonstrated on ultrasound images for medical applications.

Contribution

The paper presents a novel adaptive clipping method for NLM that uses median filtering within a statistical range to improve denoising performance over traditional NLM.

Findings

NLACM outperforms NLM at high noise levels.

Effective in speckle noise reduction in ultrasound images.

Improves true intensity estimation from noisy data.

Abstract

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.