This letter introduces a non-local means (NLM) denoising algorithm that uses a weight function based on a switching norm. The noise level and local activity are incorporated into the NLM denoising algorithm which enhances performance. This is done by selecting a norm among l1, l2, and l4 norms to determine a weighting function. The experimental results show the capability of the proposed algorithm. In addition, the proposed algorithm is verified as effective for enhancing the performance of other NLM algorithms.

The purpose of this study is to estimate the noise level of every pixel in a single noisy image, that is superimposed independent and non-identically distributed random variables with normal distribution. The method makes a set of similar pixels in the local region to the interest pixel using the approximate function of noise variance, and estimates with regard to the noise level. As the results show, the proposed method is effective in estimation of noise level of every pixel for any images.