An improved non-local means algorithm for image denoising

Abstract

Non-Local Means (NLM) denoising algorithm uses Gaussian weighted Euclidean distance to measure the similarity between neighborhoods, but when the image noise level is high, the distance measurement method is not well filtered noise. In this paper, an improved nonlocal mean filter image denoising algorithm is designed by analyzing the shortcomings of Gaussian weighted Euclidean distance in measuring neighborhood similarity. The gradient information of a filtered image is used and applied to the Gaussian weighting coefficient to achieve the purpose of adaptive filtering. The experimental results show that the algorithm has better denoising effect than traditional algorithm, especially when the noise level is high, it has high peak signal to noise ratio.